Methods and kits for diagnosing colorectal cancer

ABSTRACT

The invention pertains to a method for early detection and screening of colorectal cancer in human subjects based on RNA isolated from blood obtained from said subject. According to the invention, the abundance of at least 3, 5, 8, 30, 60, 102, 202, 55, 1002 or 1002 RNAs listed in tables 1 to 13 is measured. Using the invention, an accurate and noninvasive screening and diagnosis tool for colorectal cancer is provided with a sensitivity of at least 80% and a specificity of 85% that has high clinical utility and the potential for broad adoption.

The invention pertains to a method for diagnosing or detecting colorectal cancer in human subjects based on ribonucleic acid (RNA), in particular based on RNA from blood.

BACKGROUND

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths in the United States. Each year, approximately 150,000 people are diagnosed with CRC and almost 60,000 people die from the disease.

CRC arises from the mucosa forming the inner lining of colon and rectum. Like any other mucosa, it needs to be regenerated and proliferates at a high rate (about one third of all fecal matter are mucosa cells), and is thus susceptible to abnormal growth, i.e., neoplasia and/or dysplasia. In fact, abnormal colonic mucosal growth can be detected in about 40 of all persons over the age of 55 years. The development of neoplasia into cancer is a well-established concept in the biomedical sciences; and is termed adenoma-carcinoma-sequence (ACS).

Pathologists classify abnormal mucosal growth into four categories with increasing severity: 1) Low-grade intraepithelial neoplasia (LIEN) or adenoma, which occurs in more than 30%; 2) high-grade intraepithelial neoplasia (HIEN) or advanced adenoma, occurring in more than 2%; 3) carcinoma in situ (CIS or pTis), where the cancerous growth is still confined to the mucosa; and 4) CRC, where the cancerous growth has invaded the submucosa. CRC is diagnosed with an incidence rate of about 1% in persons over the age of 55 years with an average risk for the disease. The lifetime risk of developing CRC is estimated to be 1 in 18 persons (Cancer Statistics 2009: A Presentation From the American Cancer Society; 2009, American Cancer Society, Inc.).

After primary diagnosis of CRC, the spread/stage of the disease is classified according to the guidelines set forth by the “Union International Contre le Cancer” (UICC). UICC-stage 0 includes CIS only. UICC-stages I and II are comprised of the localized stages, whereas UICC-stage III describes CRC where tumor cells have metastasized into regional lymph nodes. The worst case is UICC-stage IV; it describes CRC which has metastasized into other organ(s), usually liver (−75%), peritoneum (−22%), and/or lung (−21%).

In 2008, the cancer registry in the state of Brandenburg/Germany documented 1591 patients with newly diagnosed CRC and stage information. They were staged into UICC-stage I: 22.6%, UICC-stage II: 29.2%, UICC-stage III: 28.9%, and UICC-stage IV: 19.0%. Relative five year survival-rates by UICC-stage were: I: 90.5%, II: 78.8%, III: 60.6%, and IV: 9.3%.

The U.S. National Institutes of Health (http://seer.cancer.gov) reported for the period 1999 to 2006 216,332 patients diagnosed with CRC with localized disease (UICC-stage I and II): 39%, regional disease (UICC stage III): 37% and distant disease (UICC-stage IV): 19%. Relative five year survival-rates by stage were: localized (UICC-stages I and II): 90.4%, regional (UICC stage III): 60.5%, and distant (UICC-stage IV): 11.6%. However, the statistics of the U.S. National Institutes of Health do not cover the U.S. population, while the data from the cancer registry in the state of Brandenburg/Germany do.

Current technologies to detect mucosal neoplasia (polyps/adenoma) and CRC can be categorized into three classes:

-   I) in-vitro diagnostics (IVDs)—a specimen/sample (blood, stool, or     urine) is taken from the test person and analyzed for one or more     biomarkers as surrogate markers for colorectal neoplasia/cancer.     Exemplary tests include guaic fecal occult blood test (gFOBT) or     immunological fecal occult blood test (iFOBT), detection of tumor     DNA-chains (deoxyribonucleic acid chains) in stool samples,     detection of specific methylated tumor DNA-chains in stool samples,     detection of specific free methylated. DNA-chains in blood plasma,     detection of elevated and/or lowered amounts of specific proteins in     blood samples, or detection of elevated and/or lowered amounts of     specific RNA-chains in blood samples; -   II) imaging methods without interventional capabilities such as     double contrast barium enema (DCBE), video capsule endoscopy, or     computed tomographic colonography; -   III) imaging methods with interventional capabilities such as     flexible sigmoidoscopy, colonoscopy, laparoscopy, or open surgery.

To obtain a definitive diagnosis of colorectal neoplasia/cancer, an invasive procedure is typically required. The procedure requires taking a sample of the visibly abnormal tissue growth (neoplasia/cancer) and having a person of skill in the art of pathology examine this sample, who will then decide (diagnose) whether this sample was taken from a neoplasia/cancer or not (Sternberg's Diagnostic Surgical Pathology (5^(th) Edition). Mills S E, Carter D, Greenson J K, Reuter V, Stoler M H. Lippincott Williams & Wilkins (LWW), 2009).

In response to the high incidence and mortality rate of patients with CRC, the American Cancer Society issued the following statement: “There are significant updates to the guidelines for colorectal cancer screening. Two new tests are now recommended as options for colorectal cancer screening. They are stool DNA (sDNA) and computerized tomographic colonography (also known as “virtual colonoscopy”). For the first time, screening tests are grouped into categories based on performance characteristics: those that primarily detect cancer early and those that can also detect precancerous polyps. Tests that primarily detect cancer early are fecal (stool) tests, including guaiac-based and immunochemical-based fecal occult blood tests (gFOBT & FIT), and stool DNA tests (sDNA). Tests that detect both precancerous polyps and cancer include flexible sigmoidoscopy, colonoscopy, the double contrast barium enema, and computerized tomographic colonography (also known as virtual colonoscopy). It is the strong opinion of the expert panel that colon cancer prevention should be the primary goal of colorectal cancer screening. Exams that are designed to detect both early cancer and precancerous polyps should be encouraged if resources are available and patients are willing to undergo an invasive test” (Cancer Statistics 2009: A Presentation From the American Cancer Society; ©2009, American Cancer Society, Inc.). A review of current CRC screening in Europe can be found in: Zavoral M, Suchanek S, Zavada F, Dusek L, Muzik J, Seifert B, Fric P. Colorectal cancer screening in Europe. World J. Gastroenterol. 2009 Dec. 21; 15: 5907-15.

However, each of the tests for the detection of mucosal neoplasias (polyps/adenomas and CRC has limitations.

For example, the imaging methods (with or without interventional capabilities) require preparation time for the test subject, specialized equipment, and specialized medical personnel. Therefore, colonoscopy and flexible sigmoidoscopy are used only in wealthy economies such as the U.S., Switzerland, and Germany as primary screening tools for early detection of CRC. Even in the U.K., France, Italy or Spain, IVDs, in most instances gFOBT, are used as a primary screening tool for colorectal cancer. Only patients with a positive IVD test result are referred to colonoscopy.

Recently, a screening program for CRC using gFOBT was initiated in the United Kingdom. All eligible persons were contacted via mail, and a test kit was delivered. Yet, just about 50% of all contacted people complied. The willingness of patients to undergo gFOBT testing in Germany has dropped from 8.2 million tests in 2001 to 4.6 million tests in 2007 (Projekt wissenschaftliche Begleitung von Früherkennungs-Koloskopien in Deutschland, Berichtszeitraum 2008-6. Jahresbericht, im Auftrag des GKV-Spitzenverbands and der Kassenärztlichen Bundesvereinigung Version 1.1, Stand: 19. February 2010, Zentralinstitut für die kassenärztliche Versorgung in der Bundesrepublik Deutschland). In the U.S., about 24.01% of all eligible patients underwent gFOBT-screening in 2000, in 2005 the rate dropped to 17.07% (U.S. National Cancer Institute, http://progressreport.cancer.gov).

Thus, the clinical utility of all stool-based CRC-screening is limited because individuals in the CRC screening population are simply unwilling to take the test repeatedly, unless they have no other choice.

The U.S. National Institutes of Health reported that compliance with endoscopy (flexible sigmoidoscopy or colonoscopy) is dependent on the education and income of the population; by 2005 37.66% of persons with less than high school education, 46.27% of persons with high school education, and 57.52% of persons with higher than high school education had ever had an endoscopy (not defined to CRC screening purposes).

Colonoscopy is an invasive procedure, which is not only inconvenient but may be associated with health risks. Approximately 3% of the individuals over 55 years undergoing colonoscopy for screening purposes have heavy bleeding incidences. Additionally, in 2 of 1,000 individuals perforation of the colon occurs. Emergency operations must be performed to correct both heavy bleeding and perforation. As a result, 2 of 10,000 individuals who undergo colonoscopy will die from these complications. The relatively high rate of accidents in combination with the time consuming bowel cleaning procedure has led to a low adoption of colonoscopy as a screening tool even in those countries where colonoscopy is paid by the health insurances.

Thus, the overall clinical utility of all endoscopy-based CRC-screening is also limited, because it is only offered in a few countries, a high percentage of the CRC screening population is unwilling to take the test, and because of the complications associated with the test.

Therefore, the clinical utility of a test for detection of colorectal neoplasia depends not only on its performance characteristics, i.e., sensitivity and specificity, but on acceptance by the patients, the medical community, and, of course, the private or public health care system that has to pay for the test.

A blood test would have the highest chance of acceptance by patients, at least in Europe, the U.S., and Japan. In terms of the medical community, a blood test would also have the highest chance of acceptance, in particular if sensitivity and specificity are convincingly high, if there is no need for preparation time, if the blood need not be processed immediately but can be shipped to a central laboratory, if the test is accepted by local regulatory authorities, and if the test is commercially available. A high level of acceptance of a test can only be achieved if the test is endorsed by CRC screening guidelines and by the general health care system.

Although blood-based colon cancer screening has been attempted, each previously reported test is inherently limited in its respective specificity and sensitivity.

For example, Han et al., (Clin Cancer Res 2008; 14, 455-460; also: WO 2007/048074 A1) reported the discovery and validation of a five-gene expression (messenger RNA) signature for detection of colorectal carcinoma. Basically, the 37 candidate genes for the signature were selected from microarray data of 16 CRC cases and 15 controls. These 37 candidate genes were evaluated on a second set of 115 samples (58 CRC, 57 controls) using quantitative real-time PCR, validating 17 genes as differentially expressed. A further gene selection step using the PCR-results revealed the 5 gene signature, which was validated on a third set of 102 samples. The predictive power of these five genes, which was evaluated using a fourth set of 92 samples, correctly identifying 88% (30 of 34) of CRC samples and only 64% (27 of 42) of non-CRC samples. The intermediate zone contained 16 samples. The performance parameters are compiled into table A.

TABLE A Estimates and Exact Confidence Limits of GeneNews ColonSentry ™ Test Exact Two-Sided 95% CI-Limits Performance Parameter N_(C) N Estimate Lower Upper Sensitivity 30 34 0.88 0.725 0.967 Specificity 27 42 0.64 0.480 0.784 Positive Predictive Value 30 45 0.67 0.510 0.800 Negative Predictive Value 27 31 0.87 0.702 0.964 Correct Classification Rate 57 76 0.75 0.637 0.842 N_(C) = Number of correctly classified cases; CI = Confidence interval; Exact confidence limits were computed using the proc FREQ of the statistics program SAS.

Provided that patients of the last validation set were a random selection of the screening population, applying the performance on a hypothetical set of 10,000 patients with an incidence of one percent and computing the performance parameters of this test yields the results shown in table B.

TABLE B Estimates and Exact Confidence Limits of GeneNews ColonSentry ™ Test applied to a Hypothetical Set of 10000 Persons Exact Two-Sided 95% CI-Limits Performance Parameter N_(C) N Estimate Lower Upper Sensitivity 73 100 0.73 0.632 0.814 Specificity 6979 9900 0.70 0.696 0.714 Positive Predictive Value 73 2994 0.02 0.019 0.031 Negative Predictive 6979 7006 1.00 0.994 0.997 Value Correct Classification 7052 10000 0.71 0.696 0.714 Rate Nc = Number of correctly classified cases; CI = Confidence interval; Exact confidence limits were computed using the proc FREQ of the statistics program SAS.

However, based on the data provided by Han, 1,739 of 10,000 patients would have an “intermediate result”. In clinical practice, this would mean that these 1,739 patients would have to undergo colonoscopy to clarify their state. However, in this computation, these 1,739 patients were regarded as having been predicted as low risk. The main disadvantage of the ColonSentry test is its relatively low sensitivity of 73% and its low specificity of 70%. Applied to a screening population of 1 million individuals this means that 2,700 individuals with undetected CRC will not be detected by the test. In addition, 300,000 individuals (30%) are diagnosed as false positive, which need to be followed up by colonoscopy. The combination of a relatively high false negative rate of 27% with a high false positive rate of 30% reduces the clinical utility of this test and impedes acceptance by the medical community and the screening population itself.

Epigenomics AG, Germany, has a CE-marked test, Epi proColon®, in the market that measures the methylation status of the Septin-9 gene and is based on detection of free somatic tumor DNA in blood serum.

TABLE C Estimates and Exact Confidence Limits of Epi proColon ® Test Exact Two-Sided 95% CI-Limits Performance Parameter N_(C) N Estimate Lower Upper Sensitivity 69 103 0.67 0.570 0.759 Specificity 135 154 0.88 0.814 0.924 Positive Predictive Value 69 88 0.78 0.684 0.865 Negative Predictive Value 135 169 0.80 0.730 0.856 Correct Classification Rate 204 257 0.79 0.739 0.842 N_(C) = Number of correctly classified cases; CI = Confidence interval; Exact confidence limits were computed using the proc FREQ of the statistics program SAS.

The product performance figures of Epi proColon® displayed in table C are cited from the companies' website (www.epigenomics.com). Table D shows the figures when the performance of the test is applied to a hypothetical screening population. Though the Epi proColon® test performs better than GeneNews' test in some performance parameters its overall sensitivity for all four stages of CRC is only 67%. This means that if 10,000 individuals are screened and the prevalence of CRC in the screening population is approximately 1%, so that 33 individuals with CRC will be missed by the test. Applied to a screening population of 1 million individuals (3.7% of the German screening population or 1.3% of the US screening population) 3,300 individuals with CRC will not be detected by the test. This high false negative rate limits significantly the clinical utility of the Epi proColon® test. The false negative rate of patients with early stage CRC (UICC I and II) that will be missed is even higher.

TABLE D Estimates and Exact Confidence Limits of Epi proColon ® Test applied to a Hypothetical Set of 10,000 Persons Exact Two-Sided 95% CI-Limits Performance Parameter N_(C) N Estimate Lower Upper Sensitivity 67 100 0.67 0.569 0.761 Specificity 8679 9900 0.88 0.870 0.883 Positive Predictive 67 1288 0.05 0.041 0.066 Value Negative Predictive 8679 8712 1.00 0.995 0.997 Value Correct Classification 8746 10000 0.87 0.868 0.881 Rate N_(C) = Number of correctly classified cases; CI = Confidence interval; Exact confidence limits were computed using the proc FREQ of the statistics program SAS.

Another blood-based test developed by OncoMethylome Science (Liege, Belgium) measures the methylation status of two Genes FOXE1 and SYNE1. The sensitivity of this two-marker test for all four stages of CRC is 56%, while the specificity is 91% (ESMO meeting, Berlin, Germany, September 2009)

All three blood tests have a significant false negative rate and do not detect a significant number of patients with CRC. The ColonSentry test has a low specificity of 70% and burdens colonoscopy facilities with a high number of false-positive test results.

Thus, there is a clear clinical need for an improved blood-based test for screening, detecting, or diagnosing colorectal cancer with high sensitivity and high specificity, which is minimally invasive so as to permit more widespread testing of the population to indicate the presence of colorectal cancer with high accuracy and therefore with a high clinical utility, and to ensure greater adherence to recommended protocols. Further, the identification of biomarkers, such as RNAs for use in such a minimally-invasive test would fulfill a longstanding need in the art.

BRIEF DESCRIPTION OF THE INVENTION

The present invention provides methods and kits for diagnosing, detecting, and screening for colorectal cancer. Particularly, the invention provides for preparing RNA expression profiles of patient blood samples, the RNA expression profiles being indicative of the presence or absence of colorectal cancer. The invention further provides for evaluating the patient RNA expression profiles for the presence or absence of one or more RNA expression signatures that are indicative of colorectal cancer.

The inventors have surprisingly found that a sensitivity of at least 75%, and a specificity of at least 85%, is reached if and only if at least 8 RNAs are measured that are chosen from the RNAs listed in table 1. In other words, measuring 8 RNAs is necessary and sufficient for the detection of colon cancer in a human subject based on RNA from a blood sample obtained from said subject by measuring the abundance of at least 8 RNAs in the sample, that are chosen from the RNAs listed in table 1, and concluding based on the measured abundance whether the subject has colon cancer or not.

In one aspect, the invention provides a method for preparing RNA expression profiles that are indicative of the presence or absence of colorectal cancer. The RNA expression profiles are prepared from patient blood samples. The number of transcripts in the RNA expression profile may be selected so as to offer a convenient and cost effective means for screening samples for the presence or absence of colorectal cancer with high sensitivity and high specificity. Generally, the RNA expression profile includes the expression level or “abundance” of from 8 to about 3000 transcripts. In certain embodiments, the expression profile includes the RNA levels of 2500 transcripts or less, 2002 transcripts or less, 1500 transcripts or less, 1002 transcripts or less, 502 transcripts or less, 250 transcripts or less, 102 transcripts of less, or 50 transcripts or less.

In such embodiments, the profile may contain the expression level of at least 8 RNAs that are indicative of the presence or absence of colorectal cancer, and specifically, as selected from table 1, or may contain the expression level of at least 10 or at least 30 RNAs selected from table 1. Where larger profiles are desired, the profile may contain the expression level or abundance of at least about 60, at least 102, at least 202, at least 502, at least 1002 RNAs, or 2002 RNAs that are indicative of the presence or absence of colorectal cancer, and such RNAs may be selected from table 1. The identities and/or combinations of genes and/or transcripts that make up or are included in expression profiles are disclosed in tables 1 to 13. In particular embodiments, the genes or transcripts include those listed in table 8 or table 13.

Such RNA expression profiles in accordance with this aspect may be evaluated for the presence or absence of an RNA expression signature indicative of colorectal cancer. Generally, the sequential addition of transcripts from table 1 to the expression profile provides for higher sensitivity and/or specificity for the detection of colorectal cancer. For example, the sensitivity of the methods provided herein may be at least 75%, or at least 80%, or at least 85%, or at least 90%. The specificity of the method may be at least 85%, or at least 90%.

In a second aspect, the invention provides a method for detecting, diagnosing, or screening for colorectal cancer. In this aspect the method comprises preparing an RNA expression profile by measuring the abundance of at least 8 RNAs in a patient blood sample, where the abundance of such RNAs are indicative of the presence or absence of colorectal cancer. The RNAs may be selected from the RNAs listed in table 1, and optionally also from the RNAs listed in table 2, and exemplary sets of such RNAs are disclosed in tables 3 to 13. The method further comprises evaluating the profile for the presence or absence of an RNA expression signature indicative of colorectal cancer, to thereby conclude whether the patient has or does not have colorectal cancer. The method generally provides a sensitivity for the detection of colorectal cancer of at least about 75%, while providing a specificity of at least about 85%.

In various embodiments, the method comprises determining the abundance of at least 30 RNAs, at least 60 RNAs, at least 102 RNAs, at least 202 RNAs, at least 502 RNAs, at least 1002 RNAs, or of at least 2002 RNAs chosen from the RNAs listed in table 1, and as exemplified in tables 3 to 13, and optionally one or more RNAs from table 2 in addition thereto, as exemplified in table 3, and then classifying the sample as being indicative of colorectal cancer, or not being indicative of colorectal cancer.

In other aspects, the invention provides kits and custom arrays for preparing the gene expression profiles, and for determining the presence or absence of colorectal cancer.

DETAILED DESCRIPTION OF THE INVENTION

The invention provides methods and kits for screening, diagnosing, and detecting colorectal cancer in human patients (subjects). “Colorectal cancer” (CRC) refers to both colorectal adenoma and colorectal carcinoma.

A colorectal adenoma is characterized by atypical growth of epithelial cells in mucosal tissue, i.e. neoplasia. Hypercellularity with enlarged, hyperchromatic nuclei, varying degrees of nuclear stratification, nuclear polymorphisms, and loss of polarity are the histologically defining features. In colorectal adenoma, this abnormal growth is confined to the mucosa; a synonym of adenoma is intraepithelial neoplasia (IEN). If this atypical growth of epithelial cells extends/invades through the muscularis mucosae, the muscle layer under the mucosa, with destruction of the usual anatomical wall, the pathologist terms this atypical growth a colorectal carcinoma.

The distinction between high- (HIEN) and low-grade (LIEN) intraepithelial neoplasia refers to the extent of the defining features.

A patient with CRC is traditionally defined as having undergone surgery/resection of colon and/or rectum for CRC and whose resection specimen has undergone examination by a board certified pathologist, who has diagnosed a colorectal carcinoma as defined above. A patient with CRC may have undergone complete colonoscopy of colon and rectum during which the examinating physician has taken a sample of suspect tissue, which in turn has undergone examination by a board-certified pathologist, who has diagnosed a colorectal carcinoma as defined above. A synonym for a patient with CRC is “CRC-case” or simply “case.”

A patient without CRC is traditionally a person that has undergone complete colonoscopy during which the examining physician, an endoscopist, has noted no abnormal tissue growth. A synonym for a patient without CRC is “non-CRC-case” or “control.” This however does not exclude that this person has any other carcinoma.

A patient with HIEN is traditionally a person that has undergone surgery/resection of colon and/or rectum for suspected CRC and whose resection specimen has undergone examination by a board certified pathologist, who has diagnosed a high-grade intraepithelial neoplasia as defined above. Alternatively, a patient with HIEN may be a person that has undergone complete colonoscopy of colon and rectum during which the examinating physician has taken a sample of suspect tissue, which in turn has undergone examination by a board certified pathologist, who has diagnosed a high-grade intraepithelial neoplasia as defined above. A synonym for a person with HIEN is “HIEN-case” or “HIEN.”

A patient with LIEN is traditionally a person that has undergone surgery/resection of colon and/or rectum for suspected CRC and whose resection specimen has undergone examination by a board certified pathologist, who has diagnosed a high-grade intraepithelial neoplasia. Alternatively, a patient with LIEN is a person that has undergone complete colonoscopy of colon and rectum during which the examinating physician has taken a sample of suspect tissue, which in turn has undergone examination by a board certified pathologist, who has diagnosed a low-grade intraepithelial neoplasia as defined above. A synonym for a person with LIEN “is LIEN-case” or “LIEN.”

As disclosed herein, the present invention provides methods and kits for screening patient samples for those that are positive for CRC, that is, in the absence of colonoscopy and/or surgery or resection of colon or rectum with pathologist's examination.

The invention relates to the determination of the abundance of RNAs to detect a colorectal cancer in a human subject, wherein the determination of the abundance is based on RNA obtained (or isolated) from whole blood of the subject or from blood cells of the subject. For example, the sample may be obtained using PAXgene (QIAGEN) or an equivalent RNA isolation system. The measurement of the abundance of RNAs in the sample is preferably performed together, i.e. sequentially or preferably simultaneously. The blood sample preferably does not contain cancer cells. Preferably, the sample comprises or consists of white blood cells.

In various aspects, the invention involves preparing an RNA expression profile from a patient sample. The method may comprise isolating RNA from whole blood, and detecting the abundance or relative abundance of selected transcripts. As used herein, the terms RNA “abundance” and RNA “expression” are used interchangeably. The “RNAs” may be defined by reference to an expressed gene, or by reference to a transcript, or by reference to a particular oligonucleotide probe for detecting the RNA (or cDNA derived therefrom), each of which is listed in table 1 for 2002 RNAs, and in table 2 for 750 RNAs that are indicative of the presence or absence of colorectal cancer. Specifically, table 1 lists such RNAs by probe ID, gene symbol, and Transcript ID, and such nucleotide sequences are publicly accessible. Table 1A gives the fold change in expression levels for the genes/transcripts listed in table 1, when measured in 64 control cases, and 55 CRC positive cases. Table 2, listing RNAs that can be measured in addition to the RNAs of table 1, lists RNAs by probe ID, gene symbol, and Transcript ID, and such nucleotide sequences are publicly accessible.

The number of transcripts in the RNA expression profile may be selected so as to offer a convenient and cost effective means for screening samples for the presence or absence of colorectal cancer with high sensitivity and high specificity. For example, the RNA expression profile may include the expression level or “abundance” of from 8 to about 3000 transcripts. In certain embodiments, the expression profile includes the RNA levels of 2500 transcripts or less, 2002 transcripts or less, 1500 transcripts or less, 1002 transcripts or less, 502 transcripts or less, 250 transcripts or less, 202 transcripts of less, 100 transcripts of less, or 50 transcripts or less. Such profiles may be prepared, for example, using custom microarrays or multiplex gene expression assays as described in detail herein.

In such embodiments, the profile may contain the expression level of at least 8 RNAs that are indicative of the presence or absence of colorectal cancer, and specifically, as selected from table 1, or may contain the expression level of at least 8, at least 10 or at least 30 RNAs selected from table 1. Where larger profiles are desired, the profile may contain the expression level or abundance of at least 60, 102, 202, 502, 1002 RNAs, or 2002 RNAs that are indicative of the presence or absence of colorectal cancer, and such RNAs may be selected from table 1, possibly together with one or more RNAs selected from table 2. Such RNAs may be defined by gene, or by transcript ID, or by probe ID, as set forth in table 1 and table 2.

The identities of genes and/or transcripts that make up, or are included in exemplary expression profiles are disclosed in tables 1 to 13. As shown herein, profiles selected from the RNAs of table 1, optionally together with RNAS of table 2, support the detection of colorectal cancer with high sensitivity and high specificity. An exemplary selection of RNAs for the RNA expression profile is shown in table 8 and in table 3.

In some embodiments, the expression profile includes the abundance of one or more intergenic RNAs. As used herein, “intergenic RNA” or “an intergenic RNA sequence” is a transcript of table 1 whose abundance in a probe is determined that does not correspond to a known gene or transcript. Such sequences are listed in table 13.

Thus, in various embodiments, the abundance of at least 8, at least 30, at least 60, at least 102, at least 202, at least 502, at least 1002, or at least 2002 distinct RNAs are measured, in order to arrive at a reliable diagnosis of colon cancer. The set of RNAs may comprise, consist essentially of, or consist of, a set or subset of RNAs exemplified in any one of tables 1 to 13. The term “consists essentially of” in this context allows for the expression level of additional transcripts to be determined that are not differentially expressed in colorectal cancer subjects, and which may therefore be used as positive or negative expression level controls or for normalization of expression levels between samples.

Such RNA expression profiles may be evaluated for the presence or absence of an RNA expression signature indicative of colorectal cancer. Generally, the sequential addition of transcripts from table 1 to the expression profile provides for higher sensitivity and/or specificity for the detection of colorectal cancer. For example, the sensitivity of the methods provided herein may be at least 75%, or at least 80%, or at least 85%, or at least 90%. The specificity of the method may be at least 85%, or at least 90%.

The present invention provides an in-vitro diagnostic test system (IVD) that is trained (as described further below) for the detection of a colorectal cancer. For example, in order to determine whether a patient has colorectal cancer, reference RNA abundance values for colon cancer positive and negative samples are determined. The RNAs can be quantitatively measured on an adequate set of training samples comprising cases and controls, and with adequate clinical information on carcinoma status, applying adequate quality control measures, and on an adequate set of test samples, for which the detection is yet to be made. With such quantitative values for the RNAs and the clinical data for the training samples, a classifier can be trained and applied to the test samples to calculate the probability of the presence or non-presence of the colorectal carcinoma.

Various classification schemes are known for classifying samples between two or more classes or groups, and these include, without limitation: Naïve Bayes, Support Vector Machines, Nearest Neighbors, Decision Trees, Logistics, Artificial Neural Networks, and Rule-based schemes. In addition, the predictions from multiple models can be combined to generate an overall prediction. Thus, a classification algorithm or “class predictor” may be constructed to classify samples. The process for preparing a suitable class predictor is reviewed in R. Simon, Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data, British Journal of Cancer (2003) 89, 1599-1604, which review is hereby incorporated by reference.

In this context, the invention teaches an in-vitro diagnostic test system (IVD) that is trained in the detection of a colorectal cancer referred to above, comprising at least 8 RNAs, which can be quantitatively measured on an adequate set of training samples comprising cases and controls, with adequate clinical information on carcinoma status, applying adequate quality control measures, and on an adequate set of test samples, for which the detection yet has to be made. Given the quantitative values for the RNAs and the clinical data for the training samples, a classifier can be trained and applied to the test samples to calculate the probability of the presence or absence of the colorectal carcinoma.

The present invention provides methods for detecting, diagnosing, or screening for colorectal cancer in a human subject with a sensitivity and specificity not previously described for a blood-based method (see FIGS. 1 to 4). Specifically, the sensitivity of the methods provided herein is at least 75%, at least 80%, at least 85%, or at least 90%. The specificity of the methods is at least 85%, or at least 90%, for example, when determined with samples of at least 122 patients with CRC and adequate samples (e.g., at least 109) of normal individuals without CRC are tested.

In another embodiment, further RNAs than the at least 8 RNAs listed in table 1 can be used according to the invention, namely preferably the RNAs listed in table 2. Put differently, the measurement of any combination of at least 8 RNAs listed in table 1 can be combined with the measurement of any combination of at least 1 RNA listed in table 2. In a preferred embodiment, the number of RNAs from table 2 used is not greater than the number of RNAs used of in table 1. For example, when 8 (10, 20) RNAs are used of table 1, not more than 8 (10, 20) RNAs of table 2 are used in addition. In further preferred embodiments, at least 8, at least 10, at least 20, at least 60, at least 100, at least 200, or at least 500 RNAs from table 2 are used together with RNAs selected from table 1.

Without wishing to be bound by any particular theory, the above finding may be due to the fact that an organism such as a human systemically reacts to the development of a colorectal tumor by altering the expression levels of genes in different pathways. The formation of cancerous tumor cells from a nonmalignant adenoma or nonmalignant polyps, the formation of high-grade intraepithelial neoplasias and the further growth and development of cancer of different stages may trigger differential expression of genes in white blood cells that are involved in both adaptive and innate immune responses, for example wound healing, inflammatory response and antibody production pathways. Although the change in expression (abundance) might be small for each gene in a particular signature, measuring a set of at least 8 genes, preferably even larger numbers such as 100, 202, 1002, 2002 or even more RNAs, for example at least 10, at least 100, at least 200, at least 1000, or at least 2000 RNAs at the same time, and optionally together with RNAs listed in table 2, allows for the detection of colorectal cancer in a human with high sensitivity and high specificity.

In this context, an RNA obtained from a subject's blood sample, i.e. an RNA biomarker, is an RNA molecule with a particular base sequence whose presence within a blood sample from a human subject can be quantitatively measured. The measurement can be based on a part of the RNA molecule, namely a part of the RNA molecule that has a certain base sequence, which allows for its detection and thereby allows for the measurement of its abundance in a sample. The measurement can be by methods known in the art, for example analysis on a solid phase device, or in solution (for example, by RT-PCR). Probes for the particular RNAs can either be bought commercially, or designed based on the respective RNA sequence.

In the method of the invention, the abundance of several RNA molecules (e.g. mRNA or pre-spliced RNA, intron-lariat RNA, micro RNA, small nuclear RNA, or fragments thereof) is determined in a relative or an absolute manner, wherein an absolute measurement of RNA abundance is preferred. The RNA abundance is, if applicable, compared with that of other individuals, or with multivariate quantitative thresholds.

The determination of the abundance of the RNAs described herein is performed from blood samples using quantitative methods. In particular, RNA is isolated from a blood sample obtained from a human subject that is to undergo CRC testing. Although the examples described herein use microarray-based methods, the invention is not limited thereto. For example, RNA abundance can be measured by in situ hybridization, amplification assays such as the polymerase chain reaction (PCR), sequencing, or microarray-based methods. Other methods that can be used include polymerase-based assays, such as RT-PCR (e.g., TAQMAN), hybridization-based assays, such as DNA microarray analysis, as well as direct mRNA capture with branched DNA (QUANTIGENE) or HYBRID CAPTURE (DIGENE).

In certain embodiments, the invention employs a microarray. A “micoroarray” includes a specific set of probes, such as oligonucleotides and/or eDNAs (e.g., expressed sequence tags, “ESTs”) corresponding in whole or in part, and/or continuously or discontinuously, to regions of RNAs that can be extracted from a blood sample of a human subject. The probes are bound to a solid support. The support may be selected from beads (magnetic, paramagnetic, etc.), glass slides, and silicon wafers. The probes can correspond in sequence to the RNAs of the invention such that hybridization between the RNA from the subject sample (or cDNA derived therefrom) and the probe occurs. In the microarray embodiments, the sample RNA can optionally be amplified before hybridization to the microarray. Prior to hybridization, the sample RNA is fluorescently labeled. Upon hybridization to the array and excitation at the appropriate wavelength, fluorescence emission is quantified. Fluorescence emission for each particular RNA is directly correlated with the amount of the particular RNA in the sample. The signal can be detected and together with its location on the support can be used to determine which probe hybridized with RNA from the subject's blood sample.

Accordingly, in certain aspects, the invention is directed to a kit or microarray for detecting the level of expression or abundance of RNAs in the subject's blood sample, where this “profile” allows for the conclusion of whether the subject has colorectal cancer or not (at a level of accuracy described herein). In another aspect, the invention relates to a probe set that allows for the detection of the RNAs associated with CRC. If these particular RNAs are present in a sample, they (or corresponding cDNA) will hybridize with their respective probe (i.e, a complementary nucleic acid sequence), which will yield a detectable signal. Probes are designed to minimize cross reactivity and false positives. In one embodiment, the probes used are given e.g. in table 1 and table 2 as so-called Affymetrix probe set ID numbers. An Affymetrix probe set ID number is an identifier that refers to a set of probes selected to represent expressed sequences on an array. An Affymetrix probe set ID number identifies each probe present on the array, as known to a person of skill in the art. From the sequence defined by an Affymetrix probe set ID number, the sequence of an RNA hybridizing with the probe can be deduced.

Thus, the invention in certain aspects provides a microarray, which generally comprises a solid support and a set of oligonucleotide probes. The set of probes generally contains from 8 to about 3,000 probes, including at least 8 probes selected from table 1 or 8, and in addition may optionally contain a set of probes from table 2. In certain embodiments, the set contains 2002 probes or less, or 1000 probes or less, 500 probes or less, or 202 probes or less. In various embodiments, at least 10, at least 30, or at least 100 probes are listed in table 1 or table 8. The set of probes may comprise, or consist essentially of, the probes listed in table 8, with optionally one or more probes from table 2. Alternatively, the set of probes includes probes that hybridize to a combination of RNAs exemplified in any one of table 4, table 5, table 6, table 7, table 8, table 9, table 10, table 11, table 12, or table 13. The microarray may comprise, e.g., about 100.000 probes, some of which may be probes for providing reference data. For example, the microarray may comprise about 10,000 to 100,000 probes providing reference data, together with the probes of table 1 and optionally table 2.

In another embodiment, the microarray may comprise further probes for detecting RNAs listed in table 2. In certain embodiments, the set of oligonucleotide probes comprises 700 probes or less, 500 probes or less, or 200 probes or less for detecting RNAs listed in table 2.

The conclusion whether the subject has colorectal cancer or not is preferably reached on the basis of a classification algorithm, which can be developed using e.g. a random forest method, a support vector machine (SVM), or a K-nearest neighbor method (K-NN), such as a 3-nearest neighbor method (3-NN), as known in the art.

From the cross-classification of the true disease state (Positive=patient with CRC and Negative=patient without CRC) as determined by a physician and the test result as determined by the classification algorithm, the following measures for binary tests can be derived (Sullivan M S. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, 2003), see table E. An example is given in table F.

TABLE E Cross-Classification of True Disease State by Test Result True Disease State Test Result Negative Positive Total Negative n₁₁ n₁₂ n_(1Σ) Positive n₂₁ n₂₂ n_(2Σ) Total n_(Σ1) n_(Σ2) n_(ΣΣ)

TABLE F Example of a Cross-Classification of True Disease State by Test Result True Disease State Test Result Negative Positive Total Negative 30 10 40 Positive 20 70 90 Total 50 80 130

“Sensitivity” (S⁺ or true positive fraction (TPF)) refers to the count of positive test results among all true positive disease states divided by the count of all true positive disease states; in terms of table E this reads: S⁺=n₂₂/n_(Σ2); the result form table F would read: S⁺=70/80=0.875. “Specificity” (S⁻ or true negative fraction (TNF)) refers to the count of negative test results among all true negative disease states divided by the count of all true negative disease states; in terms of table E this reads: S⁺=n₁₁/n_(Σ1); the result form table F would read: S⁻=30/50=0.6. “Correct Classification Rate” (CCR or true fraction (TF)) refers to the sum of the count of positive test results among all true positive disease states and count of negative test results among all true negative disease states divided by all the sum of all cases; in terms of table E this reads: CCR=(n₁₁+n₂₂)/n_(ΣΣ); the result form table F would read: CCR=(30+70)/130≈0.769230769. The measures S⁺, S⁻, and CCR address the question: To what degree does the test reflect the true disease state?

“Positive Predictive Value” (PV⁺ or PPV) refers to the count of true positive disease states among all positive test results dived by the count of all positive test results; in terms of table E this reads: PV⁺=n₂₂/n_(2Σ); the result form table F would read: PV⁺=70/90≈0.777777778. “Negative Predictive Value” (PV⁻ or NPV) refers to the count of true negative disease states among all negative test results dived by the count of all negative test results; in terms of table E this reads: PV⁻=n₁₁/n_(1ΣΣ); the result form table F would read: PV⁻=30/40=0.75. The predictive values address the question: How likely is the disease given the test results?

Exact or asymptotic confidence limits (CI) for these rates or fractions can be computed using the commercially available software package SAS (SAS Institute Inc., Cary, N.C., USA; www.sas.com) or the publicly available software package R (www.r-project.org) (for literature reference see: Agresti A, Caffo B. Simple and effective confidence intervals for proportions and differences of proportions from adding two successes and two failures. The American Statistician: 54: 280-288, 2000).

The preferred RNA molecules that can be used in combinations described herein for diagnosing and detecting colorectal cancer in a subject according to the invention can be found in table 1 and table 2. The inventors have shown that the selection of at least 8 or more RNAs of the markers listed in table 1 can be used to diagnose or detect colorectal cancer in a subject using a blood sample from that subject. The RNA molecules that can be used for detecting, screening and diagnosing colorectal cancer are selected from the RNAs provided in table 2 (optionally together with RNAs provided in table 1), 3, 4, 5, 6, 7 or 8. Also, the RNAs (e.g., at least 8, at least 10, at least 30, or more) can be selected from table 8.

Specifically, the method of the invention comprises at least the following steps: measuring the abundance of at least 8 RNAs (preferably 8 RNAs or 10 RNAs) in the sample, that are chosen from the RNAs listed in table 1, and concluding, based on the measured abundance, whether the subject has colorectal cancer or not. Measuring the abundance of RNAs may comprise isolating RNA from blood samples as described, and hybridizing the RNA or cDNA prepared therefrom to a microarray. Alternatively, other methods for determining RNA levels may be employed.

Similarly, the abundance of at least 8 RNAs (preferably up to 29 RNAs), of at least 30 RNAs (preferably up to 59 RNAs), of at least 60 RNAs (preferably up to 101 RNAs), of at least 102 RNAs (preferably up to 201 RNAs), of at least 202 RNAs (preferably up to 501 RNAs), of at least 502 RNAs (preferably ob to 1001 RNAs), of at least 1002 RNAs (preferably up to 2001 RNAs), or of at least 2002 RNAs that are chosen from the RNAs listed in table 1 can be measured in the method of the invention. In a preferred embodiment, the abundance of at least 8 RNAs (preferably up to 29 RNAs), of at least 30 RNAs (preferably up to 59 RNAs), of at least 60 RNAs (preferably up to 101 RNAs), of at least 102 RNAs (preferably up to 201 RNAs), of at least 202 RNAs (preferably up to 501 RNAs), or of at least 502 RNAs (preferably ob to 750 RNAs) that are chosen from the RNAs listed in table 2 can be measured in the method of the invention together with RNAs listed in table 1.

Three examples of a set of 8 RNAs of which the abundance can be measured in the method of the invention are listed in table 4 with the following performance data:

Sensitivity Specificity Sig. 1 84.0% 94.0% Sig. 2 83.6% 96.9% Sig. 3 81.8% 98.4%

Three examples of a set of 30 RNAs of which the abundance can be measured in the method of the invention are listed in table 5 with the following performance data:

Sensitivity Specificity Sig. 1 83.6% 85.9% Sig. 2 92.7% 98.4% Sig. 3 96.4% 98.4%

Three examples of a set of 60 RNAs of which the abundance can be measured in the method of the invention are listed in table 6 with the following performance data:

Sensitivity Specificity Sig. 1 92.7% 92.2% Sig. 2 94.6% 99.4% Sig. 3 92.7% 98.4%

Two examples of a set of 102 RNAs of which the abundance can be measured in the method of the invention are listed in table 7 with the following performance data:

Sensitivity Specificity Sig. 1 89.1% 95.3% Sig. 2 94.5% 96.9% Sig. 3 94.5% 96.9%

An example for a set of 202 RNAs of which the abundance can be measured in the method of the invention is listed in table 8. This set of 202 RNAs is particularly preferred. The performance data is as follows:

Sensitivity Specificity Sig. 1 90.1% 95.3% Sig. 2 90.1% 98.4% Sig. 3 92.7%  100%

An example for a set of 502 RNAs of which the abundance can be measured in the method of the invention is listed in table 9 with the following performance data:

Sensitivity Specificity Sig. 1 90.1% 96.6% Sig. 2 90.1% 98.4% Sig. 3 94.5% 98.4%

An example for a set of 1002 RNAs of which the abundance can be measured in the method of the invention is listed in table 10 with the following performance data:

Sensitivity Specificity Sig. 1 92.7% 96.9% Sig. 2 94.6% 98.4% Sig. 3 92.7% 98.4%

An example for a set of 2002 RNAs of which the abundance can be measured in the method of the invention is listed in table 1.

In a further embodiment of the invention, using the example of 202 RNAs, the inventors have shown that the method of the invention is very robust in its performance. Specifically, the inventors have shown that from the set of 202 RNAs as listed in table 8, replacements of individual members of the set, enlargements of the set to up to 10 times, 8 times, 6 times, 4 times or 2 times the original set size (in the present example, 202 RNAs) with arbitrary other RNAs (also of RNAs not listed in table 1), or subtractions of individual RNAs from the original set of RNAs can be performed without reducing the performance (sensitivity and specificity) of the detection method of the invention.

In particular, in one aspect of the invention, the abundance of at least 202 RNAs is measured, wherein at least 152 of the 202 measured RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 1 to 202, and up to 50 of the remaining measured RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 203 to 2002 (preferred are those shown in table 11), thereby replacing a fraction of the RNAs that were originally chosen.

Performance data of the resulting sets is as follows:

Sensitivity Specificity Sig. 1 85.5% 93.8% Sig. 2 90.1% 89.1% Sig. 3 98.4% 98.4%

In one embodiment, at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50 RNAs from table 8 are substituted with distinct RNAs listed in table 1.

In yet another embodiment, up to 70, up to 65, up to 60, up to 55, up to 50, up to 45, up to 40, up to 35, up to 30, up to 25, up to 20, up to 15 or up to 10 RNAs from table 8 are substituted with distinct RNAs listed in table 1.

In yet another aspect of the invention, the abundance of at least 1002 RNAs is measured, wherein at least 952 of the 1002 measured RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 1 to 1002, and up to 50 of the remaining RNAs are chosen from the group of RNAs that are listed in table 1 and that are referred to therein as SEQ ID NOs. 1003 to 2002 (preferred are those shown in table 12).

Performance data of three examples of resulting sets are:

Sensitivity Specificity Sig. 1 92.7% 96.9% Sig. 2 94.5% 98.4% Sig. 3 94.5% 98.4%

When the wording “at least a number of RNAs” is used, this refers to a minimum number of RNAs that are measured. It is possible to use up to 10,000 or 20,000 genes in the invention, a fraction of which can be RNAs listed in table 1. In preferred embodiments of the invention, abundance of up to 5,000, 2,500, 2,000, 1,000, 500, 250, 100, 80, 70, 60, 50, 40, 30, 20, 10, 5, 4, 3, 2, or 1 RNA of randomly chosen RNAs that are not listed in table 1 is measured in addition to RNAs of table 1 (or subsets thereof).

In a preferred embodiment, only RNAs that are mentioned in table 1 are measured.

In one aspect of the invention, a combination of at least two of the following markers can be excluded from the scope of the invention: BCNP1, CD163, CDA, MS4A1, BANK1, and MCG20553. In another aspect of the invention, a combination of at least four of the following markers can be excluded from the scope of the invention: LGALS8, VEGFA, RNF114, PHF20, SPN, AKAP13, PDZK1IP1, S100A6, CTSB, CD163, and CD302.

In another aspect of the invention, intergenic sequences were surprisingly found that can be used to detect colorectal cancer in a human subject based on RNA from blood. Without wishing to be bound by theory, it is possible that these intergenic sequences are part of pre-spliced mRNAs, alternative polyadenylation sites or part of not yet known transcripts and reflect differences in expression of the respective genes and the complexity of the transcriptome. These intergenic sequences can be found in table 1 and are characterized by the absence of a gene symbol and RefSeq Transcript ID; they are also summarized in table 13. Examples of signatures consisting only of intergenic sequences are shown, together with sensitivity and specificity values, in FIG. 4.

In a preferred embodiment, signatures of only intergenic sequences of table 13 are used as RNA. In particular, sets of at least 8, at least 30, at least 50, at least 60, at least 102, at least 202, at least 302, at least 402, such as 50, 100, 200, 400, or 408 intergenic RNAs can be used (see also FIG. 4). It is particularly surprising that non-coding RNAs can be used to detect or diagnose CRC in a subject.

Accordingly, the invention also relates to a method for the detection of colorectal cancer in a human subject based on RNA from a blood sample obtained from the subject, comprising measuring the abundance of at least 8 RNAs in the form of intergenic sequences in the sample, that are chosen from the intergenic RNAs listed in table 1 and 13 (i.e., without a gene symbol and RefSeq Transcript ID) or that are listed in table 13. In another embodiment, the present invention is directed to a method for the detection or screening of colorectal cancer in a human subject. The method entails measuring the abundance of at most 3 RNAs in the form of intergenic sequences. In a particular embodiment, the 3 RNAs are chosen from the intergenic RNAs listed in tables 1 or 13.

The present invention, in one embodiment, is directed to diagnosing and screening for CRC by measuring the abundance of intergenic RNAs, particularly the intergenic RNAs listed in table 13, or a subset thereof. For example, in one embodiment, a method of diagnosing or screening for CRC can comprise measuring the abundance of at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400 or all 408 of the intergenic sequences provided in table 13 (see also FIG. 4).

In another embodiment, a custom microarray is provided with oligonucleotide probes, designed to detect some or all of the intergenic RNAs provided in table 13. For example, in one embodiment, a microarray is provided which includes oligonucleotide probes designed to hybridize to at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400 or all 408 intergenic RNA sequences (or cDNA derived therefrom) provided in table 13.

The expression profile or abundance of RNA markers for colorectal cancer, for example the at least 8 RNAs described above, (or more RNAs as disclosed above and herein), is determined preferably by measuring the quantity of the transcribed RNA of the marker gene. This quantity of the mRNA of the marker gene can be determined for example through chip technology (microarray), (RT-) PCR (for example also on fixated material), Northern hybridization, dot-blotting, sequencing, or in situ hybridization.

The microarray technology, which is most preferred, allows for the simultaneous measurement of RNA abundance of up to many thousand RNAs and is therefore an important tool for determining differential expression (or differences in RNA abundance), in particular between two biological samples or groups of biological samples. In order to apply the microarray technology, the RNAs of the sample need to be amplified and labeled and the hybridization and detection procedure can be performed as known to a person of skill in the art.

As will be understood by those of ordinary skill in the art, the analysis can also be performed through single reverse transcriptase-PCR, competitive PCR, real time PCR, differential display RT-PCR, Northern blot analysis, sequencing, and other related methods. In general, the larger the number of markers is that are to be measured, the more preferred is the use of the microarray technology. However, multiplex PCR, for example, real time multiplex PCR is known in the art and is amenable for use with the present invention, in order to detect the presence of 2 or more genes or RNA simultaneously.

The RNA whose abundance is measured in the method of the invention can be mRNA, cDNA, unspliced RNA, or its fragments. Measurements can be performed using the complementary DNA (cDNA) or complementary RNA (cRNA), which is produced on the basis of the RNA to be analyzed, e.g. using microarrays. A great number of different arrays as well as their manufacture are known to a person of skill in the art and are described for example in the U.S. Pat. Nos. 5,445,934; 5,532,128; 5,556,752; 5,242,974; 5,384,261; 5,405,783; 5,412,087; 5,424,186; 5,429,807; 5,436,327; 5,472,672; 5,527,681; 5,529,756; 5,545,331; 5,554,501; 5,561,071; 5,571,639; 5,593,839; 5,599,695; 5,624,711; 5,658,734; and 5,700,637, each of which is hereby incorporated in its entireties.

Preferably the decision whether the subject has colon cancer comprises the step of training a classification algorithm on an adequate training set of cases and controls and applying it to RNA abundance data that was experimentally determined based on the blood sample from the human subject to be diagnosed. The classification method can be a random forest method, a support vector machine (SVM), or a K-nearest neighbor method (K-NN), such as 3-NN.

For the development of a model that allows for the classification for a given set of biomarkers, such as RNAs, methods generally known to a person of skill in the art are sufficient, i.e., new algorithms need not be developed.

The major steps of such a model are:

1) condensation of the raw measurement data (for example combining probes of a microarray to probeset data, and/or normalizing measurement data against common controls);

2) training and applying a classifier (i.e. a mathematical model that generalizes properties of the different classes (carcinoma vs. healthy individual) from the training data and applies them to the test data resulting in a classification for each test sample.

For example, the raw data from microarray hybridizations can first be condensed with FARMS as shown by Hochreiter (2006, Bioinformatics 22(8): 943-9). Alternative methods for condensation such as Robust Multi-Array Analysis (RMA, GC-RMA, see Irizarry et al (2003). Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. 4, 249-264) can be used. Similar to condensation, classification of the test data set through a support-vector-machine or other classification algorithms is known to a person of skill in the art, like for example classification and regression trees, penalized logistic regression, sparse linear discriminant analysis, Fisher linear discriminant analysis, K-nearest neighbors, shrunken centroids, and artificial neural networks (see Wladimir Wapnik: The Nature of Statistical Learning Theory, Springer Verlag, New York, N.Y., USA, 1995; Berhard Schölkopf, Alex Smola: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, Mass., 2002; S. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques, Informatica Journal 31 (2007) 249-268).

The key component of these classifier training and classification techniques is the choice of RNA biomarkers that are used as input to the classification algorithm.

In a further aspect, the invention refers to the use of a method as described above and herein for the detection of colorectal cancer in a human subject, based on RNA from a blood sample.

In a further aspect, the invention also refers to the use of a microarray for the detection of colorectal cancer in a human subject based on RNA from a blood sample. According to the invention, such a use can comprise measuring the abundance of at least 8 RNAs (or more, as described above and herein) that are listed in table 1, optionally together with at least one RNA from table 2. Accordingly, the microarray comprises at least 8 probes for measuring the abundance of the at least 8 RNAs. It is preferred that the microarray has a set of 11 probes for each RNA, but 1 or 3 probes for each RNA are also preferred. Commercially available microarrays, such as from Affymetrix, may be used. Alternatively, at most 8, at most 10, or at most 20 RNAs are measured in a sample, in order to detect or diagnose CRC.

In another embodiment, the abundance of the at least 8 RNAs is measured by multiplex RT-PCR. In a further embodiment, the RT-PCR includes real time detection, e.g., with fluorescent probes such as Molecular beacons or TaqMan® probes.

In a preferred embodiment, the microarray comprises probes for measuring only RNAs that are listed in table 1 (or subsets thereof).

In yet a further aspect, the invention also refers to a kit for the detection of colorectal cancer in a human subject based on RNA obtained from a blood sample. Such a kit comprises a means for measuring the abundance of at least 8 RNAs that are chosen from the RNAs listed in table 1, optionally together with at least one RNA from table 2. In a further embodiment, the at least 8 RNAs are chosen from the RNAs listed in any of the tables provided herein, for example, the RNAs are chosen from table 8 or 3. The kit may further comprise a means for measuring the abundance of at least one RNA that is chosen from the RNAs listed in table 2. The means for measuring expression can be probes that allow for the detection of RNA in the sample or primers that allow for the amplification of RNA in the sample. Ways to devise probes and primers for such a kit are known to a person of skill in the art.

Further, the invention refers to the use of a kit as described above and herein for the detection of colorectal cancer in a human subject based on RNA from a blood sample comprising means for measuring the abundance of at least 8 RNAs that are chosen from the RNAs listed in table 1, and optionally further means for measuring the abundance of at least 1 RNA that is chosen from the RNAs listed in table 2. Such a use may comprise the following steps: contacting at least one component of the kit with RNA from a blood sample from a human subject, measuring the abundance of at least 8 RNAs (or more as described above and herein) that are chosen from the RNAs listed in table 1 using the means for measuring the abundance of at least 8 RNAs, and concluding, based on the measured abundance, whether the subject has colorectal cancer.

In yet a further aspect, the invention also refers to a method for preparing an RNA expression profile that is indicative of the presence or absence of colorectal cancer, comprising: isolating RNA from a whole blood sample, and determining the level or abundance of from 8 to about 3000 RNAs, including at least 8 RNAs selected from table 1, optionally further including at least 1 RNA selected from table 2.

Preferably, the expression profile contains the level or abundance of 2002 RNAs or less, of 1002 RNAs or less, of 502 RNAs or less, or of 202 RNAs or less. Further, it is preferred that at least 10 RNAs, at least 30 RNAs, at least 102 RNAs are listed in table 1 or table 8. It is preferred that the expression profile includes the level or abundance of the RNAs listed in table 8. It is also preferred that the expression profile also includes the level or abundance of RNAs listed in table 2.

In a further preferred embodiment of the method, the abundance of at least 50 RNAs provided in table 13 is measured. Another preferred embodiment of the method comprises determining the presence or absence of an RNA expression signature indicative of colorectal cancer.

In yet a further aspect, the invention also refers to a microarray, comprising a solid support and a set of oligonucleotide probes, the set containing from 8 to about 3,000 probes, and including at least 8 probes selected from table 1 or 8. Preferably, the set contains 2002 probes or less, 1000 probes or less, 500 probes or less, or 202 probes of less. At least 10 probes can be those listed in table 1 or table 8. At least 30 probes can be those listed in table 1 or table 8. In another embodiment, at least 102 probes are listed in table 1 or table 8. Further, it is preferred that the set of probes is listed in table 8. In another embodiment of the microarray, it contains at least 50 probes from table 13. Further, it is preferred that the set of probes also contains probes for detecting RNAs listed in table 2.

Features of the invention that were described herein in combination with a method, a microarray, a kit, or a use also refer, if applicable, to all other aspects of the invention.

Tables

Table 1 shows a list of 2002 RNAs that are differentially expressed in several human subjects with colorectal cancer in comparison to subject without colorectal cancer. Each marker is characterized by a SEQ ID NO, by an Affymetrix probe set ID, and, if applicable, a HUGO ID and a Ref Seq ID. The first 202 RNAs shown are an exemplary set of RNAs and are also shown in table 8.

Table 1A shows the changes of expression level (abundance) between cases and controls for all 2002 RNAs listed in table 1. The data was derived from 119 samples, 55 CRC cases and 64 controls. In the third column, the log-2 fold change is shown, and in the fourth column, the non-log fold change is shown.

The numbers in the third column represent the differences of cases (events) and controls (non-events) in log 2 steps. For example, a value of 0.53 for the first probe set (SEQ ID No.1) means an expression increase by 2̂0.53-fold for events vs. non-events. In other words, the events had a 144% expression (see fourth column) with respect to non-events, i.e. 44% more RNA (0.44=2̂0.53−1, 1.44=2̂0.53)_(.)

Table 2 shows a list of 750 RNAs that are differentially expressed in several human subjects with colorectal cancer in comparison to subjects without colorectal cancer. Each marker is characterized by a SEQ ID NO., by a probe set ID, and, if applicable, a gene symbol and a primary transcript ID. According to the invention, the abundance of at least 8 RNAs from the list of RNAs shown in table 1 is measured, optionally together with a number of RNAs taken from the list of RNAs of table 2. Examples of signatures consisting of RNAs from table 1 and table 2 are given in table 3.

Table 3 shows nine exemplary subsets consisting of RNAs listed in table 1 and table 2 (“combination signatures”).

It was found that any group of 8 or more RNAs from table 1 can be supplemented by a subset of RNAs from table 2. Combination signatures 1 through 9 of table 3 show exemplary combinations of subsets from tables 1 and 2 with at least 8 RNAs from table 1. Three different sizes of sets were used:

-   -   less than 100 RNAs in combination signatures 1, 2, and 3     -   100 to 500 RNAs in combination signatures 4, 5, and 6     -   500 to 1000 RNAs in combination signatures 7, 8, and 9.

In each of these three size ranges, the first set was balanced in favor of RNAs from table 1 regarding RNA numbers, the second was balanced between numbers of RNAs of table 1 and table 2, and the third set was balanced in favor of table 2.

The following table shows the subset sizes for combination signatures 1 through 9 of table 3:

Number of Number of Combination RNAs from RNAs from signature Table 1 Table 2 1 22 8 2 8 8 3 8 24 4 256 101 5 100 100 6 91 261 7 620 275 8 275 272 9 269 633

The performance of the combination signature, determined as average over 1000 test runs, where each time randomly chosen 15 percent of the samples were used as test set, is as follows:

Combination signature Sensitivity Specificity 1 80.8% 85.4% 2 83.6%, 87.0% 3 77.3%, 81.5% 4 88.5%, 96.1% 5 90.4%, 92.3% 6 88.6%, 94.8% 7 90.9%, 96.3% 8 87.8%, 96.0% 9 88.7%, 95.9%

Table 4 shows three exemplary subsets of 8 RNAs of the RNAs listed in table 1. Performance (obtained using the leave-one-out method) (specificity/sensitivity): Sig1: 84%/94%, Sig2: 83.6%/96.9%, Sig3: 81.8%/98.45%.

Table 5 shows three exemplary subsets of 30 RNAs of the RNAs listed in table 1. Performance (obtained using the leave-one-out method) (specificity/sensitivity):

Sig1: 83.6%/85.9%, Sig2: 92.7%/98.4%; Sig3: 96.4%/98.4%.

Table 6 shows three exemplary subsets of 60 RNAs of the RNAs listed in table 1. Performance (obtained using the leave-one-out method) (specificity/sensitivity):

Sig 1: 92.7%/92.2%, Sig 2: 94.6%/98.4%, Sig 3: 92.7%/98.4%.

Table 7 shows two exemplary subsets of 102 RNAs of the RNAs listed in table 1. Performance (obtained using the leave-one-out method) (specificity/sensitivity):

Sig 1: 89.1%/95.3%, Sig 2: 94.5%/96.9%, Sig 3: 94.5%/96.9%

Table 8 shows one exemplary subsets of 202 RNAs of the RNAs listed in table 1. Performance (specificity/sensitivity): Sig.1: 90.1%/95. % Sig.2: 90.1%/98.4%; Sig.3: 92.7%/100%).

Table 9 shows one exemplary subsets of 502 RNAs of the RNAs listed in table 1. Performance (specificity/sensitivity):

Sig.1: 90.1%/96.9%, Sig.2: 90.1%/98.4%; Sig.3: 94.5%/98.4%.

Table 10 shows one exemplary subset of 1002 RNAs of the RNAs listed in table 1. Performance (obtained using the leave-one-out method) (specificity/sensitivity):

Sig1: 92.7%/96.9%, Sig2: 94.6%/98.4%, Sig3: 92.7%/98.4%.

Table 11 shows an exemplary set of RNAs for substitution of 50 RNAs from a set of 202 RNAs. Performance (obtained using the leave-one-out method) (specificity/sensitivity): Exchange 1: 85.5%/93.8%, Exchange 2: 90.1%/89.1%, Exchange 3: 98.4%/98.4%.

Table 12 shows an exemplary set of RNAs for substitution of 50 RNAs from a set of 1002 RNAs. Performance (obtained using the leave-one-out method) (specificity/sensitivity): Exchange 1: 92.7%/96.9%, Exchange 2: 94.5%/98.4%, Exchange 3: 94.5%/98.4%.

Table 13 shows a subset of the RNAs listed in table 1, namely those 408 RNAs that comprise an intergenic sequence or consist of an intergenic sequence. These RNAs are listed in table 1 without a gene name. Examples for performances of signatures consisting of RNAs from table 13 are shown in FIG. 4.

FIGURES

FIG. 1 is a graph showing various RNA colorectal cancer signatures as a function of performance. The x-axis shows the number of RNAs in each particular signature subset. The subset length is varied along the x-axis from 8 to 202 (the full set). The y-axis shows performance of the subset. Performance is shown in terms of sensitivity (percentage of real carcinomas that were properly classified; lower values (squares)) and specificity ((percentage of real health controls that were properly classified; upper values (diamonds)) scaled from 50% to 100%. These are retrospective examinations of the 202 set. As the figure shows, reduced sets compared to the full set appear to have excellent performance. For each subset size, the performance was measured for 1000 randomly chosen subsets based on leave-16%-out runs: the subset is trained on 84% and applied to the remaining 16%. The average performance over all the 16% tests is shown.

FIG. 2 is a graph showing the performance of RNA colorectal cancer signatures, each having 202 RNAs. Various RNAs from the first signature in table 8 (first column) were replaced with random subsets of RNA markers from table 1 that are not listed in table 8. The number of replacements is given along the x-axis and performance is given on the y-axis. Performance of the method of detecting CRC is shown in terms of sensitivity (percentage of real carcinomas that were properly classified; lower values (squares)) and specificity (percentage of real health controls that were properly classified upper values (diamonds)) scaled from 50% to 100%. For each subset size, the performance was measured for 1000 randomly chosen subsets based on leave-16%-out runs: the subset is trained on 84% and applied to the remaining 16%. The average performance over all the 16% tests is shown. As the figure shows, replacements do not alter the performance of the signature given in table 8.

FIG. 3 is a graph showing the performance of RNA colorectal cancer signatures. The signatures each contain the RNAs from table 8, and also include additional RNAs from table 1 that are not in table 8. The resulting signature contains the original 202 elements and the new elements. The extension size (original length+length of addition) is varied along the x-axis from 202 to 2002. Performance (y-axis) is shown in terms of sensitivity (percentage of real carcinomas that were properly classified; lower values (squares)) and specificity (percentage of real health controls that were properly classified; upper values (diamonds)), scaled from 50% to 100%. These are retrospective examinations of the 202 set. As the figure demonstrates, larger sets compared to the set with 202 RNAs appear to have excellent performance. For each subset size, the performance was measured for 1000 randomly chosen subsets based on leave-16%-out runs: the subset is trained on 84% and applied to the remaining 16%. The average performance over all the 16% tests is shown.

FIG. 4 is a graph showing the performance of RNA colorectal cancer signatures of 50, 100, 200, and 400 RNAs, wherein the RNAs are all intergenic sequences. The signatures each contain the RNAs from table 13. The performance of, from left to right, the first 50, 100, 200, and 400 RNAs is shown. Each light column (on the left of each pair of two columns) represents the sensitivity, each dark column (on the right of each pair of two columns) represents the specificity for each signature.

EXAMPLES Materials and Methods

Study Protocol

All results described herein are based on a prospective, clinical-diagnostic study protocol entitled Früherkennung kolorektaler Karzinome mit Hilfe von RNA-basierten Expression-Signaturen aus Blut-Eine multizentrische, diagnostische Studie zur Entwicklung and Validierung von Genexpressions-Signaturen zur Früherkennung kolorektaler Karzinome-Version CRC.SCR.2.BR.1 vom 06. January 2009” in accordance with the Guideline for Good Clinical Practice (Directive 75/318/EEC) July 1996, version July 2002 (http://www.emea.eu.int/pdfs/human/ich/013595en.pdf). This study protocol was reviewed and approved by the local ethics authority, the “Ethik-Kommission der Landesärztekammer Brandenburg” on Jan. 14, 2009. All persons entered into this study gave written informed consent that their blood samples and associated clinical data could be used for this research endeavor. Moreover, the persons gave written, informed consent that samples and clinical data could be audited by regulatory authorities if the research results would be used in a performance evaluation study (according to German law (Gesetz über Medizinprodukte)).

This study was designed as a cohort study. One cohort, the colonoscopy cohort, included persons undergoing colonoscopy. The second cohort, the surgery cohort, included patients scheduled for surgery for suspected colorectal carcinoma.

The inclusion criteria for the colonoscopy cohort were: 1) Willingness to undergo complete colonoscopy; 2) At least 55 years of age; 3) Ability to give written, informed consent; 4) Written informed consent after information about the study was given by a physician. The exclusion criteria for the colonoscopy cohort were: 1) Rectoscopy, sigmoidoscopy, or colonoscopy during the last five years prior to inclusion into the study; 2) Treatment of a malignant disease during the last five years prior to inclusion into the study, except for malignoma with low metastatic potential such as basalioma in situ of the skin.

The inclusion criteria for the surgery cohort were: 1) Age at initial diagnosis at least 18 years of age; 2) Ability to give written, informed consent; 3) (Suspected) Diagnosis of colorectal carcinoma UICC-stage I to IV; 4) Surgery is planned in such a fashion that staging according to UICC-criteria is feasible; 5) No treatment prior to surgery; 6) No treatment for a malignant disease during the last five years prior to inclusion into the study; 7) No other disease that lowers life expectancy below one year; 8) Regular follow-up examinations have to be possible; 9) Written informed consent after information about the study was given by a physician.

Blood Drawing and Blood Sample Storage

In the colonoscopy cohort, blood was drawn after written, informed consent and prior to bowel cleaning and change of medication if necessary. In the surgery cohort, blood was drawn after written, informed consent and prior to removal of the resected tissue from the body of the patient by the surgeon. In both cohorts, colonoscopy cohort and surgery cohort, blood was drawn into PAXgene™ blood tubes (Becton Dickinson). The tubes were stored within four hours in a freezer at −20° C. until transport to the laboratory on dry ice, where the tubes were stored again in a freezer at −20° C. until RNA-extraction.

Sample Sizes

The study protocol initially stipulated the use of 220 blood samples from the colonoscopy cohort and 220 blood samples from the surgery cohort for the discovery of the signatures. However, the study protocol was open to changes depending on the results of the discovery process. Additionally, the study protocol initially stipulated the use 220 blood samples from the colonoscopy cohort and 220 blood samples from the surgery cohort for prospective performance evaluation purposes. Again, these samples sizes were open and amenable to change.

Quality Control of Clinical Data

All clinical data of all included persons from both cohorts were checked directly from the patient's medical records and entered into study databases, as prescribed in the study protocol. Each item in the study databases were checked independently by two persons. Patients of both cohorts together with their blood samples were withdrawn if any violation of the inclusion of exclusion criteria cited above was detected. In particular for the colonoscopy cohort, all samples and their associated clinical data were excluded from all analyses if the colonoscopy was found to be not complete. Moreover, blood samples were destroyed and clinical data were deleted if the patient decided to withdraw his/her informed consent.

RNA Extraction

Total RNA extraction from blood was performed using the QIAGEN PAXgene Blood miRNA Kit together with the QIAcube® robot according to the manufacturer's instructions. The RNA obtained is therefore sometimes referred to as PAX-RNA.

Before starting RNA extraction, the buffers BM3 and BM4 were mixed with the specified amount of 96-100% Ethanol. Furthermore, the DNAse I (1500 Kunitz units) was dissolved in 550 μl of RNAse free water.

After thawing, the PAX-blood tubes were turned several times to ensure proper mixing and left over night (or minimal two hours) at room temperature. Then, the tubes were centrifuged for 10 minutes at 4000×g using a swing out bucket. Next, the supernatant was carefully removed from each tube, and the pellets were washed (vortexing until complete resuspension) with 4 ml RNAse free water. After centrifuging again for 10 minutes at 4000×g, the pellets were resuspended in 350 μl buffer BR1 and transferred to 2 ml processing tubes. The opened tubes were then loaded into the rotor adapters of the centrifuge. Subsequently, all buffers were placed on the respective spots of the reagent bottle holder. After closing the lid, the PAXgene Blood miRNA Part A protocol was started. When it was finished, the instrument door was opened, the RNA containing tubes were closed and subsequently placed on the shaker adaptor. After closing the instrument door again, the PAXgene Blood miRNA Part B protocol was started. When the program was finished, concentration was determined by UV-absorption measurement and the samples were stored at −70° C. until use.

For understanding of underlying principles of the automatic procedure the manual protocol is briefly described below. There is no difference between the two methods until the resuspension of the pellet.

To the resuspended pellet 300 μl binding buffer (BR2) and 40 μl proteinase K solution was added and mixed by vortexing for 5 seconds. Incubation follows for 10 minutes at 55° C. using a shaker-incubator at 400-1400 rpm. After incubation, the temperature of the shaker-incubator is set to 65° C. The lysate is pipetted directly into a PAXgene Shredder spin column (lilac) placed in a 2 ml processing tube and centrifuged for 3 minutes at maximum speed (don't not to exceed 20,000×g). The entire supernatant of the flow-through fraction was carefully transferred the to a fresh 1.5 ml microcentrifuge tube without disturbing the pellet in the processing tube. 350 μl ethanol (96-100%, purity grade p.a.) was added and mixed by vortexing. The mixture is briefly (1-2 seconds at 500-1000×g) centrifuged to remove drops from the inside of the tube Lid. 700 μl of the sample is pipetted into the PAXgene RNA spin column (red) placed in a 2 ml processing tube, and centrifuged for 1 minute at 8,000-20,000×g. The spin column was placed in a new 2 ml processing tube (PT), and the old processing tube containing flow-through discarded. Subsequently, the remaining sample was pipetted into the PAXgene RNA spin column, and centrifuged for 1 minute at 8,000-20,000×g. The spin column was placed in a new 2 ml processing tube and the old processing tube containing flow-through again discarded. Subsequently, 350 μl wash buffer 1 (BR3) was pipetted into the PAXgene RNA spin column. After centrifugation for 1 minute at 8000-20,000×g the spin column was placed in a new 2 ml processing tube and the old processing tube containing the flow-through again discarded. 10 μl DNase I stock solution is added to 70 μl DNA digestion buffer in a 1.5 ml microcentrifuge tube and mixed by gently flicking the tube followed by a brief centrifugation to collect residual liquid from the sides of the tube. The DNase I incubation mix (80 μl) was pipetted directly onto the PAXgene RNA spin column membrane, and placed on the benchtop (20-30° C.) for 15 minutes. After Incubation, 350 μl wash buffer 1 (BR3) is pipetted into the PAXgene RNA spin column and centrifuged for 1 minute at 8000-20,000×g. The spin column was placed in a new 2 ml processing tube and the old processing tube containing flow-through again discarded. 500 μl wash buffer 2 (BR4) was pipetted into the PAXgene RNA spin column and centrifuged for 1 minute at 8,000-20,000×g. The spin column was placed in a new 2 ml processing tube and the old processing tube containing flow-through again discarded. Another 500 μl wash buffer 2 (BR4) was added to the PAXgene RNA spin column and centrifuged for 3 minutes at 8,000-20,000×g. The spin column is placed in a new 2 ml processing tube and the old processing tube containing flow-through again discarded. The column is centrifuged for 1 minute at 8,000-20,000×g. The processing tube is discarded and the column was placed on a 1.5 ml microcentrifuge tube. 40 μl elution buffer (BR5) directly pipetted onto the PAXgene RNA spin column membrane and subsequently centrifuged for 1 minute at 8,000-20,000×g to elute the RNA. The elution step is repeated using again 40 μl elution buffer (BR5) and the same 1.5 ml microcentrifuge tube. The RNA is denatured for 5 minutes at 65° C. in the shaker-incubator (see above) without shaking.

The quality control is performed on the Agilent Bioanalyzer.

Total RNA (100 ng) was labeled and hybridized onto Affymetrix U133Plus 2.0 GeneChips (Affymetrix; Santa Clara, Calif.) according to the manufacturer's instructions. Briefly, the 100 ng total RNA was used for cDNA synthesis with the Ovation® Whole Blood system (Nugen, San Carlos, Calif. 94070). After SPIA™ Amplification the cDNA was purified with a QIAquick PCR purification spin column (QIAGEN, Hilden) and then subjected to Biotin labeling with the FL-Ovation™ cDNA Biotin Module V2 (Nugen).

5 μg cDNA was then added into the hybridization cocktail and the cocktail was applied to the probe array cartridge. After 16 hours hybridization, the array was washed with Affymetrix fluidics station 450. The array was then scanned with Affymetrix® GeneChip® Scanner. Hybridization signals were collected with the Affymetrix GCOS software (version 1.4), using the default settings and imported into R (Bioconductor package)

Recruitment

499 persons scheduled for colonoscopy were recruited into the study; RNA blood samples were taken prior to colonoscopy. The recruitment period for the non-CRC patients lasted from Feb. 9, 2009 until Apr. 3, 2009. In parallel, RNA blood samples were taken from patients with (suspected) diagnosis of CRC prior or during surgery for CRC.

Discovery Set-Up

Sample Selection for RNA-Extraction

The selection criteria of CRC-cases included availability of UICC stage information and the check that the patients did not fulfill any exclusion criteria for the MSKK-study. At this time clinical data of the non-CRC case were not available. Hence, the first 240 patients of the study were selected.

Altogether, 480 PAX-RNA samples, 240 from non-CRC persons and 240 from CRC patients, were randomized into the discovery set. Additionally, the order of processing by the laboratory was randomized. A drop-out rate of 10%, or 20 cases in each group, was assumed.

Results of RNA-Extraction

From the RNA-extraction samples, 14 samples (2.92%) showed RNA integrity numbers (RIN) lower than 3.5, which rendered these samples unfit for microarray hybridization.

Selection of Samples for Microarray Hybridization

Based on the results of RNA extraction and clinical information about UICC-stages and complete colonoscopy in case of non-CRC cases, 428 samples were authorized for microarray hybridization. The vast majority of drop-outs were incomplete colonoscopies.

Microarray Pseudonymization

A pseudonymization procedure was set-up at the beginning of the hybridization. On each day microarrays were hybridized, the system administrator removed the raw scan image files from the scan computer system and placed them in a directory. The system administrator applied the program (GCOS) to compute the expression (abundance) values from the raw image data and stored the computed expression values of each probe in a so-called .CEL-file. When the days' microarray production was complete and the probe expression values were all computed, the system administrator informed the project leader that the days' production was ready for pseudonomyzation. Through a program, the original .CEL-file was copied and the filename was stored within the .CEL-file, and was exchanged with a pseudonym.

Moreover, the date and time of the scan which are also stored in the .CEL-file was also replaced, since from date and time of scan the sample would be identifiable. Lastly, the pseudonomized .CEL-files was copied into a directory accessible by the bioinformatics group and authorizes these .CEL-files for microarray quality control.

Microarray Quality Control

The microarray data authorized for quality control were checked according to a Standard Operating Procedure (SOP), and a report was given to the project leader. As of now, two scans of 128 scans (1.56%; exact two-sided 95% CI: 0.19%-5.53%), one of a MAQC control sample and one of a clinical sample did not pass quality control.

Discovery Procedure

In brief, all raw measurement data from microarray hybridizations scheduled for a discovery (i.e., from samples selected for discovery, and measurements that passed quality control) were condensed together with the FARMS algorithm as shown by Hochreiter et al 2006 (“A new summarization method for Affymetrix probe level data.” Bioinformatics 2006 22(8):943-949, doi:10.1093/bioinformatics/bt1033), and filtered using IN/I calls shown by Talloen et al., 2007 (“I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data.” Bioinformatics 2007; 23(21): 2897-2902 doi:10.1093/bioinformatics/btm478). During this discovery, FARMS version 1.4 was used as part of the statistical software systems R (version 2.9.0) and Bioconductor (version 1.6) (for this software, see http://cran.r-project.org/, http://www.r-project.org/ and http://www.bioconductor.org, also Ross Ihaka and Robert Gentleman. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3): 299-314, 1996; Gentleman et al 2005: Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer, New York, N.Y.).

The resulting condensed array data were then partitioned into groups of samples in a double nested bootstrap approach (Efron (1979) Bootstrap Methods—Another Look at the Jackknifing, Ann. Statist. 7, 1-6). In the outer loop of this bootstrap, the samples were partitioned into an outer test set and an outer training set. In the inner bootstrap loop, this outer training set was partitioned again into an inner training set and an inner test set.

On the inner training set, probeset relevance was estimated through a decision-tree-analysis. The influence of each feature was determined from its contribution to the classification error: In case the error of a probeset increases due to the permutation of the values of a probeset while the values of all other probesets remain unchanged, this probeset is weighted more strongly.

The relevance evaluations of the individual inner loops were combined for each external loop and the chosen probesets were used to train a support vector machine (Bernhard Schölkopf, Alex Smola: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, Mass., 2002, also see citations above). This classifier was trained on the outer training set (which was already used for its feature selection) and applied to the outer test set of samples. For this classification, the same methods and parameters were use as for the intended application on external data (for example as part of validation): the details are described below as application for the RNAs of the invention. The whole external loop of the bootstrap procedure is a simulation of later classification of unknown data. Its average performance over all external loops gives a prospective estimate of the performance of the classification procedure (mainly based on the chosen biomarker set). The common mistake of overfitting, i.e., the overly optimistic evaluation of a classification method on its discovery set, is thereby avoided.

Finally, the results of all inner loops and of all external loops were combined to form a common biomarker set with prospective estimates of performance.

Application of the Invention

A number of RNAs that result from the discovery above can be used with state of the art classification methods as described in the cited literature and as known by a person of skill in the art. As any classification, it will require a representative training set, which the inventors obtained through a clinical study fulfilling the requirements described above. Part of the training set is the necessary clinical information (carcinoma patient or healthy control, as defined by the clinical study). Similar to the clinical requirements, this description of the algorithmic part of the application of the invention also presupposes the described lab process and quality controls were applied.

The training set of microarray raw data was condensed by the same method as the discovery set in the section “Discovery Procedure” above: the FARMS condensation algorithm, preferentially version 1.4 (available at http://www.bioinf.jku.at/software/farms/farms.html from the author of the software, citations to the publication of this algorithm see above). A standard implementation of support vector machines, such as the one contained in Bioconductor's (see references above) package for machine learning “e1071” with parameter “kernel” set to “linear” (preferential version for this package is 1.5-19).

It is important not to present the classification information on the training data as numeric data, but as categorical data. This can be ensured by passing the corresponding arguments as an R “factor” (for example as in “svm( . . . , as.factor(clinicalData), . . . )”. Otherwise this svm algorithm will use the wrong type of classification.

To apply this svm, using the same software package, to any kind of new microarray data, a condensation step is necessary just as for the discovery data. It is possible to simply reapply the condensation method above to each individual new sample in combination with the whole discovery set. This first method approximates the preferential method well, which is to explicitly compute a condensation model or parameter based on the discovery data and apply it to the test data. This can be done with the software package on.farms, preferentially version 1.4.1, available from the author (see references above). The application of the described svm to the thus condensed test data produces the desired decision value.

The embodiments illustrated and discussed in this specification are intended only to teach those skilled in the art the one way to make and use the invention. Modifications and variation of the above-described embodiments of the invention are possible without departing from the invention, as appreciated by those skilled in the art in light of the above teachings. It is therefore understood that, within the scope of the claims and their equivalents, the invention may be practiced otherwise than as specifically described.

TABLE 1 SEQ Gene RefSeq Transcript ID No. Probe Set ID Symbol ID 1 1560255_at C10ORF31 XR_041926 2 238712_at — — 3 227970_at GPR157 NM_024980 4 237056_at INSC NM_001042536.1 5 241981_at FAM20A NM_017565.2 6 223974_at MGC11082 XR_079560.1 7 241415_at — — 8 1565544_at RNF141 NM_016422.3 9 216693_x_at HDGFRP3 NM_016073.2 10 1570264_at — — 11 221044_s_at TRIM6-TRIM34 NM_001003818.1 12 203658_at SLC25A20 NM_000387 13 213390_at ZC3H4 NM_015168.1 14 224832_at DUSP16 NM_030640 15 212642_s_at HIVEP2 NM_006734.3 16 225005_at PHF13 NM_153812.2 17 242167_at — — 18 221143_at RPA4 NM_013347.3 19 243824_at — — 20 222139_at KIAA1466 AB040899 21 237315_at — — 22 238360_s_at — — 23 239571_at — — 24 232356_at — — 25 224645_at EIF4EBP2 NM_004096.4 26 1556110_at — — 27 234842_at TRA@ — 28 212017_at FAM168B NM_001009993.2 29 226772_s_at SAP30L NM_024632.5 30 1552648_a_at TNFRSF10A NM_003844.2 31 225840_at TEF NM_003216.2 32 202657_s_at SERTAD2 NM_014755.2 33 231764_at CHRAC1 NM_017444.4 34 232513_x_at C20ORF107 NM_001013646 35 233283_at — — 36 206983_at CCR6 NM_031409.3 37 243654_at — — 38 223558_at C9ORF80 NM_021218.1 39 225263_at HS6ST1 NM_004807.2 40 1563497_at USP25 NM_013396.3 41 237655_at — — 42 230571_at PLLP NM_015993 43 238439_at ANKRD22 NM_144590.2 44 200666_s_at DNAJB1 NM_006145.1 45 235024_at PHF17 NM_199320.2 46 244025_at ITPRIP NM_033397.2 47 217868_s_at METTL9 NM_001077180.1 48 236229_at ZNF814 NM_001144989.1 49 202365_at UNC119B NM_001080533.1 50 208741_at SAP18 NM_005870.4 51 226585_at NEIL2 NM_001135747.1 52 219938_s_at PSTPIP2 NM_024430.3 53 1554557_at ATP11B NM_014616.1 54 244492_at — — 55 243303_at — — 56 216112_at — — 57 225494_at DYNLL2 NM_080677.2 58 239363_at C9ORF80 NM_021218.1 59 1563975_at RNF130 NM_018434.4 60 242917_at RASGEF1A NM_145313.2 61 240661_at LOC284475 — 62 226547_at MYST3 NM_001099413.1 63 231259_s_at CCND2 NM_001759.3 64 210281_s_at ZMYM2 NM_003453.2 65 235543_at IRF2 — 66 202656_s_at SERTAD2 NM_014755 67 243399_at HAAO NM_012205.2 68 205321_at EIF2S3 NM_001415.3 69 230738_at HIST2H4B NM_001034077.4 70 234302_s_at ALKBH5 NM_017758.3 71 231309_at GNA12 NM_007353.2 72 201751_at JOSD1 NM_014876.5 73 221654_s_at USP3 NM_006537.2 74 235058_at GPN2 NM_018066.3 75 203609_s_at ALDH5A1 NM_170740.1 76 239845_at — — 77 223637_s_at C11ORF56 NM_032127.3 78 228156_at — — 79 243509_at — — 80 238053_at DHRSX NM_145177.2 81 240733_at — — 82 235421_at MAP3K8 NM_005204.2 83 204787_at VSIG4 NM_007268.2 84 212806_at PRUNE2 NM_015225.2 85 200759_x_at NFE2L1 NM_003204.2 86 213174_at TTC9 NM_015351.1 87 205181_at ZNF193 NM_006299.3 88 242306_at — — 89 1562321_at PDK4 NM_002612.3 90 201578_at PODXL NM_005397.3 91 1554132_a_at FAM190B NM_018999 92 225545_at EEF2K NM_013302.3 93 228811_at TMEM8B NM_001042590.1 94 203879_at PIK3CD NM_005026.3 95 201170_s_at BHLHE40 NM_003670.2 96 215322_at — — 97 1562110_at — — 98 226804_at FAM20A NM_017565 99 1554116_s_at PARP11 NM_020367.4 100 237942_at — — 101 219671_at HPCAL4 NM_016257.2 102 210104_at MED6 NM_005466.2 103 212097_at CAV1 NM_001172895 104 1558761_a_at FAM120AOS NM_198841.2 105 215888_at PDS5B NM_015032.2 106 208823_s_at CDK16 NM_006201 107 201731_s_at TPR NM_00329 108 220131_at FXYD7 NM_022006.1 109 216233_at CD163 NM_004244.4 110 236629_at C1ORF69 NM_001010867.2 111 1562260_at — — 112 33494_at ETFDH NM_004453 113 228068_at GOLGA7B NM_001010917.1 114 222640_at DNMT3A NM_022552.3 115 239077_at GALNACT2 NM_018590 116 207040_s_at ST13 NM_003932.3 117 228723_at — — 118 1562031_at JAK2 NM_004972 119 218771_at PANK4 NM_018216.1 120 219906_at FLJ10213 NM_018029.3 121 217286_s_at NDRG3 NM_032013.2 122 235777_at ANKRD44 NM_153697.1 123 218625_at NRN1 NM_016588.2 124 1567213_at PNN NM_002687.3 125 243276_at ALS2CL NM_147129.2 126 215210_s_at DLST NM_001933.3 127 207339_s_at LTB NM_002341.1 128 202533_s_at DHFR NM_000791.3 129 230267_at WSB1 NM_015626.8 130 218168_s_at CABC1 NM_020247.4 131 222844_s_at SRR NM_021947.1 132 233264_at — — 133 214501_s_at H2AFY NM_001040158.1 134 203505_at ABCA1 NM_005502.2 135 203556_at ZHX2 NM_014943.3 136 226474_at NLRC5 NM_032206.3 137 215640_at TBC1D2B NM_015079.5 138 243902_at — — 139 208109_s_at C15ORF5 NR_026813.1 140 220905_at — — 141 201259_s_at SYPL1 NM_006754.2 142 221908_at RNFT2 NM_001109903.1 143 205379_at CBR3 NM_001236.3 144 229374_at EPHA4 NM_004438.3 145 1569666_s_at — — 146 228216_at SERPINC1 NM_000488 147 240217_s_at GMEB1 NM_024482.1 148 218287_s_at EIF2C1 NM_012199.2 149 218517_at PHF17 NM_024900 150 244796_at GLS NM_014905.3 151 233976_at — — 152 233647_s_at CDADC1 NM_030911.1 153 239489_at UBLCP1 NM_145049.3 154 238923_at SPOP NM_001007226.1 155 237018_at AKAP13 NM_007200.3 156 240176_at — — 157 200860_s_at CNOT1 NM_016284.3 158 212639_x_at TUBA1C NM_032704.3 159 201236_s_at BTG2 NM_006763.2 160 205857_at SLC18A2 NM_003054.3 161 228603_at ACTR3 NM_005721.3 162 238728_at MRPS23 NM_016070.2 163 216729_at — — 164 206674_at FLT3 NM_004119.2 165 238025_at MLKL NM_152649.2 166 203255_at FBXO11 NM_025133.3 167 213294_at CCDC75 NM_174931.2 168 215209_at SEC24D NM_014822.2 169 208030_s_at ADD1 NM_176801.1 170 238570_at ZNF611 NM_030972.2 171 232164_s_at EPPK1 NM_031308.1 172 209828_s_at IL16 NM_172217.2 173 222526_at GATAD2A NM_017660.3 174 203366_at POLG NM_002693.2 175 242319_at DGKG NM_001080745.1 176 223846_at AZI2 NM_022461.3 177 234402_at TRD@ M18414 178 32042_at ENOX2 NM_182314.1 179 225478_at MFHAS1 NM_004225.2 180 229754_at — — 181 243514_at — — 182 213484_at ADD2 NM_001617.2 183 208744_x_at HSPH1 NM_006644.2 184 1558592_at — — 185 239462_at ZNF284 NM_001037813.2 186 212399_s_at VGLL4 NM_014667.2 187 236013_at CACNA1E NM_000721.2 188 211986_at AHNAK NM_001620.1 189 242803_at — — 190 217815_at SUPT16H NM_007192.3 191 219120_at C2ORF44 NM_025203.2 192 242780_at VAPA NM_003574.5 193 242695_at — — 194 1570394_at XRN1 NM_019001.3 195 221639_x_at HNRNPU NM_031844.2 196 217185_s_at ZNF259 NM_003904.3 197 1568964_x_at SPN NM_001030288.1 198 1556942_at — — 199 200594_x_at HNRNPU NM_031844.2 200 1559739_at CHPT1 NM_020244.2 201 232157_at SPRY3 NM_005840.1 202 224855_at PYCR2 NM_013328.2 203 200796_s_at MCL1 NM_021960 204 201747_s_at SAFB NM_002967 205 205781_at C16orf7 NM_004913 206 201032_at BLCAP NM_006698 207 226687_at PRPF40A NM_017892 208 207253_s_at UBN1 NM_001079514 209 222310_at SFRS15 NM_001145444 210 203218_at MAPK9 NM_001135044 211 203409_at DDB2 NM_000107 212 217857_s_at RBM8A NM_005105 213 212428_at KIAA0368 NM_001080398 214 213266_at TUBGCP4 NM_014444 215 201652_at COPS5 NM_006837 216 204211_x_at EIF2AK2 NM_001135651 217 201051_at ANP32A NM_006305 218 230998_at — — 219 230664_at H2BFM NM_001164416 220 200979_at PDHA1 NM_000284 221 228623_at — — 222 200767_s_at FAM120A NM_014612 223 201721_s_at LAPTM5 NM_006762 224 200030_s_at SLC25A3 NM_002635 225 215828_at — — 226 237943_at — — 227 1569369_at ZFYVE28 NM_020972 228 204216_s_at ZC3H14 NM_001160103 229 202796_at SYNPO NM_001109974 230 226656_at CRTAP NM_006371 231 212234_at ASXL1 NM_015338 232 209715_at CBX5 NM_001127321 233 200016_x_at HNRNPA1 NM_002136 234 209078_s_at TXN2 NM_012473 235 204275_at SOLH NM_005632 236 1566501_at — — 237 228091_at STX17 NM_017919 238 1554096_a_at RBM33 NM_053043 239 209358_at TAF11 NM_005643 240 1554628_at ZNF57 NM_173480 241 203379_at RPS6KA1 NM_001006665 242 1569999_at — — 243 232628_at — — 244 219004_s_at C21orf45 NM_018944 245 222043_at CLU NM_001831 246 212527_at PPPDE2 NM_015704 247 235114_x_at HOOK3 NM_032410 248 232188_at AKAP13 NM_006738 249 209735_at ABCG2 NM_004827 250 207604_s_at SLC4A7 NM_003615 251 224990_at C4orf34 NM_174921 252 235840_at — — 253 232648_at PSMA3 NM_002788 254 220213_at TSHZ2 NM_173485 255 218912_at GCC1 NM_024523 256 219963_at DUSP13 NM_001007271 257 214783_s_at ANXA11 NM_001157 258 213836_s_at WIPI1 NM_017983 259 202184_s_at NUP133 NM_018230 260 237317_at — — 261 214016_s_at SFPQ NM_005066 262 236901_at — — 263 204025_s_at PDCD2 NM_002598 264 208740_at SAP18 NM_005870 265 201083_s_at BCLAF1 NM_001077440 266 208087_s_at ZBP1 NM_001160417 267 202941_at NDUFV2 NM_021074 268 215545_at — — 269 201088_at KPNA2 NM_002266 270 1556007_s_at — — 271 212859_x_at MT1E NM_175617 272 204245_s_at RPP14 NM_001098783 273 210965_x_at CDC2L5 NM_003718 274 204116_at IL2RG NM_000206 275 219409_at SNIP1 NM_024700 276 204962_s_at CENPA NM_001042426 277 221535_at LSG1 NM_018385 278 228200_at ZNF252 NR_023392 279 203905_at PARN NM_001134477 280 212415_at 38961 NM_015129 281 232427_at ZNF224 NM_013398 282 226052_at BRD4 NM_014299 283 227346_at IKZF1 NM_006060 284 201588_at TXNL1 NM_004786 285 1561880_a_at SIGLEC16 NR_002825 286 221529_s_at PLVAP NM_031310 287 224686_x_at LRRC37A2 NM_001006607 288 201762_s_at PSME2 NM_002818 289 210732_s_at LGALS8 NM_006499 290 229897_at ZNF641 NM_152320 291 203836_s_at MAP3K5 NM_005923 292 220439_at RIN3 NM_024832 293 213193_x_at TRBC1 — 294 225125_at MMGT1 NM_173470 295 1555884_at PSMD6 NM_014814 296 210277_at AP4S1 NM_001128126 297 223946_at MED23 NM_004830 298 212346_s_at MXD4 NM_006454 299 203799_at CD302 NM_014880 300 228158_at LOC645166 NR_027238 301 238140_at ARV1 NM_022786 302 236703_at NT5C2 NM_001134373 303 1565651_at ARF1 NM_001024226 304 200988_s_at PSME3 NM_005789 305 206652_at ZMYM5 NM_001039649 306 202717_s_at CDC16 NM_001078645 307 1554424_at FIP1L1 NM_001134937 308 231557_at — — 309 1557520_a_at — — 310 200751_s_at HNRNPC NM_001077442 311 209299_x_at PPIL2 NM_014337 312 238704_at — — 313 1555906_s_at C3orf23 NM_001029839 314 232034_at LOC203274 — 315 1568907_at — — 316 232330_at C7orf44 NM_018224 317 1563687_a_at FRYL NM_015030 318 226107_at — — 319 214472_at HIST1H2AD NM_003530 320 208978_at CRIP2 NM_001312 321 222975_s_at CSDE1 NM_001007553 322 232569_at — — 323 230735_at — — 324 203497_at MED1 NM_004774 325 219138_at RPL14 NM_001034996 326 200815_s_at PAFAH1B1 NM_000430 327 233545_at INPP5D NM_001017915 328 208876_s_at PAK2 NM_002577 329 204533_at CXCL10 NM_001565 330 241752_at SLC8A1 NM_001112800 331 202768_at FOSB NM_001114171 332 234991_at ZXDC NM_001040653 333 228818_at — — 334 218805_at GIMAP5 NM_018384 335 219001_s_at DCAF10 NM_024345 336 202386_s_at KIAA0430 NM_014647 337 228624_at TMEM144 NM_018342 338 236971_at — — 339 202840_at TAF15 NM_003487 340 209124_at MYD88 NM_002468 341 210136_at MBP NM_001025081 342 202003_s_at ACAA2 NM_006111 343 238788_at LOC494150 — 344 243598_at — — 345 222909_s_at BAG4 NM_004874 346 218641_at LOC65998 NM_001144936 347 239104_at MGC42157 XR_017710 348 1558354_s_at — — 349 238257_at MLLT10 NM_001009569 350 1568830_at IRAK3 NM_001142523 351 231937_at — — 352 212766_s_at ISG20L2 NM_030980 353 1569652_at MLLT3 NM_004529 354 223832_s_at CAPNS2 NM_032330 355 227249_at NDE1 NM_001143979 356 209162_s_at PRPF4 NM_004697 357 210943_s_at LYST NM_000081 358 213963_s_at SAP30 NM_003864 359 233079_at MERTK NM_006343 360 242945_at FAM20A NM_017565 361 239213_at SERPINB1 NM_030666 362 226229_s_at SSU72 NM_014188 363 212124_at ZMIZ1 NM_020338 364 224357_s_at MS4A4A NM_024021 365 236273_at NBPF1 NM_017940 366 204526_s_at TBC1D8 NM_001102426 367 233263_at — — 368 209907_s_at ITSN2 NM_006277 369 201880_at ARIH1 NM_005744 370 218448_at C20orf11 NM_017896 371 243631_at MPHOSPH8 NM_017520 372 238026_at RPL35A NM_000996 373 218454_at PLBD1 NM_024829 374 202172_at VEZF1 NM_007146 375 209161_at PRPF4 NM_004697 376 201470_at GSTO1 NM_004832 377 206335_at GALNS NM_000512 378 204061_at PRKX NM_005044 379 203368_at CRELD1 NM_001031717 380 235309_at RPS15A NM_001019 381 226874_at KLHL8 NM_020803 382 217905_at C10orf119 NM_024834 383 215175_at PCNX NM_014982 384 1555793_a_at ZFP82 NM_133466 385 235681_at — — 386 243315_at — — 387 205475_at SCRG1 NM_007281 388 239902_at — — 389 216134_at FRMD4B NM_015123 390 235119_at TAF3 NM_031923 391 1553333_at C1orf161 NM_152367 392 203113_s_at EEF1D NM_001130053 393 235518_at SLC8A1 NM_001112800 394 217555_at SMC1A NM_006306 395 227854_at — — 396 225613_at MAST4 NM_015183 397 201739_at SGK1 NM_001143676 398 201960_s_at MYCBP2 NM_015057 399 221571_at TRAF3 NM_003300 400 221819_at RAB35 NM_006861 401 203298_s_at JARID2 NM_004973 402 1558459_s_at LOC401320 XM_379482 403 57516_at ZNF764 NM_033410 404 202714_s_at KIAA0391 NM_014672 405 1559614_at FLJ38773 XR_001008 406 223027_at SNX9 NM_016224 407 201927_s_at PKP4 NM_001005476 408 200699_at KDELR2 NM_001100603 409 223189_x_at MLL5 NM_018682 410 215507_x_at — — 411 219385_at SLAMF8 NM_020125 412 218039_at NUSAP1 NM_001129897 413 1567101_at — — 414 1563629_a_at — — 415 217894_at KCTD3 NM_016121 416 212814_at AHCYL2 NM_001130720 417 205692_s_at CD38 NM_001775 418 233186_s_at BANP NM_017869 419 219308_s_at AK5 NM_012093 420 1556750_at LOC153577 — 421 203676_at GNS NM_002076 422 234865_at LOC100293705 XM_002346051 423 209555_s_at CD36 NM_000072 424 200633_at UBB NM_018955 425 203065_s_at CAV1 NM_001753 426 235959_at — — 427 218047_at OSBPL9 NM_024586 428 218358_at CRELD2 NM_001135101 429 228138_at ZNF498 NM_145115 430 226913_s_at SOX8 NM_014587 431 1560705_at — — 432 232821_at GTSF1L NM_001008901 433 239679_at — — 434 237953_at — — 435 220560_at C11orf21 NM_001142946 436 220797_at METT10D NM_024086 437 219112_at RAPGEF6 NM_001164386 438 235061_at PPM1K NM_152542 439 212945_s_at MGA NM_001080541 440 218232_at C1QA NM_015991 441 213604_at TCEB3 NM_003198 442 229659_s_at — — 443 212794_s_at KIAA1033 NM_015275 444 218969_at Magmas NM_016069 445 1555487_a_at ACTR3B NM_001040135 446 207129_at CA5B NM_007220 447 244578_at LCP2 NM_005565 448 225407_at MBP NM_001025081 449 230515_at — — 450 232559_at — — 451 244726_at — — 452 238644_at MYSM1 NM_001085487 453 203240_at FCGBP NM_003890 454 236399_at — — 455 223492_s_at LRRFIP1 NM_001137550 456 203234_at UPP1 NM_003364 457 213260_at FOXC1 NM_001453 458 216142_at — — 459 233072_at NTNG2 NM_032536 460 228660_x_at SEMA4F NM_004263 461 238107_at — — 462 201376_s_at HNRNPF NM_001098204 463 210317_s_at YWHAE NM_006761 464 218426_s_at RNF216 NM_207111 465 239637_at — — 466 224336_s_at DUSP16 NM_030640 467 244659_at LOC100131015 XM_001726042 468 1555950_a_at CD55 NM_000574 469 219284_at HSPBAP1 NM_024610 470 236007_at AKAP10 NM_007202 471 212601_at ZZEF1 NM_015113 472 233781_s_at RIF1 NM_018151 473 238109_at — — 474 201305_x_at ANP32B NM_006401 475 210592_s_at SAT1 NM_002970 476 204140_at TPST1 NM_003596 477 227599_at C3orf59 NM_178496 478 1554287_at TRIM4 NM_033017 479 243470_at — — 480 1562898_at — — 481 239287_at — — 482 1558938_at — — 483 1557780_at — — 484 207734_at LAX1 NM_001136190 485 214595_at KCNG1 NM_002237 486 236982_at — — 487 1555608_at CAPRIN2 NM_001002259 488 228923_at S100A6 NM_014624 489 207968_s_at MEF2C NM_001131005 490 203927_at NFKBIE NM_004556 491 222306_at — — 492 203130_s_at KIF5C NM_004522 493 219498_s_at BCL11A NM_018014 494 1557224_at — — 495 223313_s_at MAGED4 NM_001098800 496 1555180_at LOC100132686 XR_078450 497 203632_s_at GPRC5B NM_016235 498 220387_s_at HHLA3 NM_001031693 499 217791_s_at ALDH18A1 NM_001017423 500 1559479_at PI4K2B NM_018323 501 1554600_s_at LMNA NM_005572 502 1569792_a_at C12orf72 NM_001135863 503 235071_at WDR92 NM_138458 504 243334_at CACNA1D NM_000720 505 234848_at — — 506 201489_at PPIF NM_005729 507 210189_at HSPA1L NM_005527 508 221816_s_at PHF11 NM_001040443 509 231319_x_at KIF9 NM_001134878 510 202863_at SP100 NM_001080391 511 235699_at REM2 NM_173527 512 211930_at HNRNPA3 NM_194247 513 228349_at — — 514 224516_s_at CXXC5 NM_016463 515 204099_at SMARCD3 NM_001003801 516 221589_s_at ALDH6A1 NM_005589 517 1569617_at OSBP2 NM_030758 518 212904_at LRRC47 NM_020710 519 203007_x_at LYPLA1 NM_006330 520 236165_at MSL3 NM_006800 521 229404_at TWIST2 NM_057179 522 222234_s_at DBNDD1 NM_001042610 523 200659_s_at PHB NM_002634 524 226668_at WDSUB1 NM_001128212 525 219669_at CD177 NM_020406 526 208778_s_at TCP1 NM_001008897 527 203320_at SH2B3 NM_005475 528 226802_s_at LOC96610 NR_027293 529 1565928_at — — 530 212259_s_at PBXIP1 NM_020524 531 231816_s_at UBE2Q1 NM_017582 532 228184_at DISP1 NM_032890 533 222821_s_at GEMIN7 NM_001007269 534 217722_s_at NGRN NM_001033088 535 1566171_at RFFL NM_001017368 536 233219_at — — 537 1565894_at — — 538 1569599_at SAMSN1 NM_022136 539 206761_at CD96 NM_005816 540 1556253_s_at — — 541 1564053_a_at YTHDF3 NM_152758 542 229199_at SCN9A NM_002977 543 226734_at EIF4E2 NM_004846 544 226447_at ASH1L NM_018489 545 202985_s_at BAG5 NM_001015048 546 208180_s_at HIST1H4H NM_003543 547 217995_at SQRDL NM_021199 548 234172_at — — 549 205169_at RBBP5 NM_005057 550 212387_at TCF4 NM_001083962 551 213293_s_at TRIM22 NM_006074 552 244536_at — — 553 232306_at CDH26 NM_021810 554 212838_at DNMBP NM_015221 555 235033_at NPEPL1 NM_024663 556 203460_s_at PSEN1 NM_000021 557 209409_at GRB10 NM_001001549 558 216527_at — — 559 1554453_at HNRPLL NM_001142650 560 226775_at ENY2 NM_020189 561 211152_s_at HTRA2 NM_013247 562 235825_at — — 563 212594_at PDCD4 NM_014456 564 206059_at ZNF91 NM_003430 565 214184_at NPFF NM_003717 566 205392_s_at CCL14 NM_004166 567 213046_at PABPN1 NM_004643 568 234883_x_at TRBV7-3 — 569 228650_at — — 570 227112_at TMCC1 NM_001017395 571 217431_x_at CYBB NM_000397 572 202095_s_at BIRC5 NM_001012270 573 206026_s_at TNFAIP6 NM_007115 574 226651_at HOMER1 NM_004272 575 240859_at — — 576 1555634_a_at LILRA5 NM_021250 577 235207_at — — 578 231695_at — — 579 213524_s_at G0S2 NM_015714 580 212318_at TNPO3 NM_012470 581 227329_at ZBTB46 NM_025224 582 203050_at TP53BP1 NM_001141979 583 219975_x_at OLAH NM_001039702 584 215671_at PDE4B NM_001037339 585 231260_at BC036928 — 586 217513_at C17orf60 NM_001085423 587 1556339_a_at — — 588 208774_at CSNK1D NM_001893 589 221536_s_at LSG1 NM_018385 590 231641_at FLJ10213 NM_018029 591 202551_s_at CRIM1 NM_016441 592 204110_at HNMT NM_001024074 593 201235_s_at BTG2 NM_006763 594 219633_at TTPAL NM_001039199 595 227055_at METTL7B NM_152637 596 234427_at TRAJ17 — 597 239819_at — — 598 230762_at — — 599 201651_s_at PACSIN2 NM_007229 600 244579_at — — 601 233476_at — — 602 1562576_at — — 603 227589_at PITPNC1 NM_012417 604 209062_x_at NCOA3 NM_006534 605 236755_at TBC1D23 NM_018309 606 223608_at EFCAB2 NM_001143943 607 204079_at TPST2 NM_001008566 608 238789_at KANK1 NM_015158 609 213172_at TTC9 NM_015351 610 224582_s_at NUCKS1 NM_022731 611 243796_at LOC100293311 XM_002345033 612 213567_at — — 613 242197_x_at CD36 NM_000072 614 244177_at — — 615 210873_x_at APOBEC3A NM_145699 616 238929_at SFRS2B NM_032102 617 229756_at — — 618 224681_at GNA12 NM_007353 619 242423_x_at — — 620 219300_s_at CNTNAP2 NM_014141 621 209477_at EMD NM_000117 622 203088_at FBLN5 NM_006329 623 242947_at — — 624 210609_s_at TP53I3 NM_004881 625 203660_s_at PCNT NM_006031 626 213755_s_at — — 627 216297_at — — 628 1559375_s_at — — 629 217866_at CPSF7 NM_001136040 630 223337_at SDCCAG10 NM_005869 631 221524_s_at RRAGD NM_021244 632 216614_at — — 633 220342_x_at EDEM3 NM_025191 634 218883_s_at MLF1IP NM_024629 635 217486_s_at ZDHHC17 NM_015336 636 236572_at — — 637 203188_at B3GNT1 NM_006876 638 1564231_at IFT80 NM_020800 639 209765_at ADAM19 NM_023038 640 202902_s_at CTSS NM_004079 641 224369_s_at FBXO38 NM_030793 642 218876_at TPPP3 NM_015964 643 240099_at — — 644 228119_at LRCH3 NM_032773 645 239130_at — — 646 1560971_a_at — — 647 222820_at TNRC6C NM_001142640 648 243476_at NF1 NM_000267 649 1558697_a_at KIAA0430 NM_014647 650 1558046_x_at LOC100132864 XM_001713861 651 241275_at CAPZA1 NM_006135 652 209425_at AMACR NM_014324 653 228835_at — — 654 237901_at — — 655 234785_at — — 656 1557193_at — — 657 244447_at — — 658 204950_at CARD8 NM_014959 659 227410_at FAM43A NM_153690 660 205500_at C5 NM_001735 661 228394_at STK10 NM_005990 662 1561206_at KLHL8 NM_020803 663 48808_at DHFR NM_000791 664 228253_at LOXL3 NM_032603 665 227501_at — — 666 226674_at SHISA4 NM_198149 667 202290_at PDAP1 NM_014891 668 237062_at — — 669 1554665_at ZNF586 NM_017652 670 237082_at — — 671 201206_s_at RRBP1 NM_001042576 672 1561615_s_at SLC8A1 NM_001112800 673 201783_s_at RELA NM_001145138 674 212180_at CRKL NM_005207 675 220289_s_at AIM1L NM_001039775 676 225157_at MLXIP NM_014938 677 237627_at — — 678 203052_at C2 NM_000063 679 215030_at GRSF1 NM_001098477 680 208629_s_at HADHA NM_000182 681 201757_at NDUFS5 NM_004552 682 232251_at NUDT16P NR_002949 683 228468_at MASTL NM_032844 684 226989_at RGMB NM_001012761 685 1556257_at LOC645513 XR_078309 686 223194_s_at SLC22A23 NM_015482 687 212166_at XPO7 NM_001100161 688 240233_at — — 689 1563053_at LOC729083 XM_001133289 690 1568915_at — — 691 223121_s_at SFRP2 NM_003013 692 204067_at SUOX NM_000456 693 226313_at C10orf35 NM_145306 694 206636_at RASA2 NM_006506 695 212457_at TFE3 NM_006521 696 1553856_s_at P2RY10 NM_014499 697 230885_at SPG7 NM_003119 698 231357_at CLEC12B NM_001129998 699 241956_at — — 700 201596_x_at KRT18 NM_000224 701 244026_at — — 702 211072_x_at TUBA1B NM_006082 703 230619_at ARNT NM_001668 704 1554833_at MCTP2 NM_001159643 705 212388_at USP24 NM_015306 706 1566809_a_at — — 707 235310_at GCET2 NM_001008756 708 211003_x_at TGM2 NM_004613 709 230036_at SAMD9L NM_152703 710 239391_at FAM120AOS NM_198841 711 214243_s_at SERHL NM_014509 712 240806_at RPL15 NM_002948 713 1554447_at LOC554203 NR_024582 714 242501_at — — 715 209865_at SLC35A3 NM_012243 716 238961_s_at FNDC3A NM_001079673 717 1557465_at RP11- NR_027047 327P2.4 718 1562608_at — — 719 214394_x_at EEF1D NM_001130053 720 230796_at LOC440900 XR_041709 721 220546_at MLL NM_005933 722 210640_s_at GPER NM_001039966 723 1562745_at — — 724 1556323_at CUGBP2 NM_001025076 725 213208_at KIAA0240 NM_015349 726 201143_s_at EIF2S1 NM_004094 727 202087_s_at CTSL1 NM_001912 728 1558279_a_at KDSR NM_002035 729 223591_at RNF135 NM_032322 730 202005_at ST14 NM_021978 731 242031_at — — 732 204889_s_at NEURL NM_004210 733 217701_x_at — — 734 213774_s_at — — 735 235288_at — — 736 204222_s_at GLIPR1 NM_006851 737 214513_s_at CREB1 NM_004379 738 201678_s_at C3orf37 NM_001006109 739 1563348_at — — 740 1561306_s_at STRC NM_153700 741 223733_s_at PPP4R1L NR_003505 742 203308_x_at HPS1 NM_000195 743 232121_at TRDMT1 NM_004412 744 1556211_a_at — — 745 232033_at USP37 NM_020935 746 202601_s_at HTATSF1 NM_001163280 747 212680_x_at PPP1R14B NM_138689 748 1563466_at MYLK NM_053025 749 219676_at ZSCAN16 NM_025231 750 1558586_at ZNF33B NM_006955 751 230886_at — — 752 240144_at DNASE1 NM_005223 753 1560034_a_at — — 754 221223_x_at CISH NM_013324 755 200999_s_at CKAP4 NM_006825 756 235456_at — — 757 214766_s_at AHCTF1 NM_015446 758 202706_s_at UMPS NM_000373 759 233176_at — — 760 227449_at EPHA4 NM_004438 761 225655_at UHRF1 NM_001048201 762 236024_at GPM6A NM_005277 763 207654_x_at DR1 NM_001938 764 236094_at TCF7L2 NM_001146274 765 230166_at KIAA1958 NM_133465 766 229008_at WDR60 NM_018051 767 210817_s_at CALCOCO2 NM_005831 768 1560069_at PLEKHM3 NM_001080475 769 236113_at — — 770 202794_at INPP1 NM_001128928 771 231174_s_at — — 772 1559459_at LOC613266 NM_001033516 773 238892_at — — 774 203803_at PCYOX1 NM_016297 775 221801_x_at NEFL NM_006158 776 222449_at PMEPA1 NM_020182 777 202954_at UBE2C NM_007019 778 1568997_at — — 779 228769_at ZSCAN22 NM_181846 780 1557119_a_at ZNF575 NM_174945 781 200857_s_at NCOR1 NM_006311 782 234082_at — — 783 243819_at — — 784 206632_s_at APOBEC3B NM_004900 785 203430_at HEBP2 NM_014320 786 1555626_a_at SLAMF1 NM_003037 787 1559618_at LOC100129447 XM_001719594 788 1555058_a_at LPGAT1 NM_014873 789 221253_s_at TXNDC5 NM_001145549 790 219493_at SHCBP1 NM_024745 791 238607_at ZNF296 NM_145288 792 215411_s_at TRAF3IP2 NM_001164281 793 203616_at POLB NM_002690 794 206075_s_at CSNK2A1 NM_001895 795 232953_at C20orf69 NM_001104925 796 226652_at USP3 NM_006537 797 222413_s_at MLL3 NM_170606 798 214814_at YTHDC1 NM_001031732 799 228983_at — — 800 215342_s_at RABGAP1L NM_001035230 801 227737_at SRPRB NM_021203 802 233033_at ZEB2 NM_014795 803 209378_s_at FAM190B NM_018999 804 209105_at NCOA1 NM_003743 805 221833_at LONP2 NM_031490 806 239516_at — — 807 205798_at IL7R NM_002185 808 200868_s_at RNF114 NM_018683 809 1564639_at LOC389906 XM_001713861 810 203633_at CPT1A NM_001031847 811 223588_at THAP2 NM_031435 812 202349_at TOR1A NM_000113 813 200600_at MSN NM_002444 814 201196_s_at AMD1 NM_001033059 815 208642_s_at XRCC5 NM_021141 816 231340_at — — 817 210253_at HTATIP2 NM_001098520 818 212624_s_at CHN1 NM_001025201 819 228478_at — — 820 1569154_a_at — — 821 1553857_at IGSF22 NM_173588 822 224567_x_at MALAT1 NR_002819 823 221215_s_at RIPK4 NM_020639 824 200710_at ACADVL NM_000018 825 220302_at MAK NM_005906 826 214499_s_at BCLAF1 NM_001077440 827 238836_at — — 828 213256_at MARCH3 NM_178450 829 226095_s_at ATXN1L NM_001137675 830 203816_at DGUOK NM_080916 831 209307_at SWAP70 NM_015055 832 201144_s_at EIF2S1 NM_004094 833 236707_at DAPP1 NM_014395 834 240703_s_at HERC1 NM_003922 835 229720_at BAG1 NM_004323 836 238037_at LMLN NM_001136049 837 200597_at EIF3A NM_003750 838 207563_s_at OGT NM_181672 839 241441_at — — 840 217961_at SLC25A38 NM_017875 841 225696_at COPS7B NM_022730 842 211721_s_at ZNF551 NM_138347 843 212095_s_at MTUS1 NM_001001924 844 242857_at — — 845 241421_at — — 846 225119_at CHMP4B NM_176812 847 243375_at — — 848 202239_at PARP4 NM_006437 849 200819_s_at RPS15 NM_001018 850 237218_at — — 851 1570124_at — — 852 217225_x_at NOMO1 NM_001004060 853 215220_s_at TPR NM_003292 854 228899_at LOC100132884 XR_078381 855 222880_at AKT3 NM_005465 856 218820_at C14orf132 NR_023938 857 223892_s_at TMBIM4 NM_016056 858 229907_at — — 859 209536_s_at EHD4 NM_139265 860 240899_at — — 861 221267_s_at FAM108A1 NM_001130111 862 216069_at — — 863 215390_at — — 864 224583_at COTL1 NM_021149 865 205248_at DOPEY2 NM_005128 866 227820_at TBC1D25 NM_002536 867 208934_s_at LGALS8 NM_006499 868 229364_at LOC646870 XR_040282 869 222073_at COL4A3 NM_000091 870 219757_s_at C14orf101 NM_017799 871 226013_at TRAK1 NM_001042646 872 239922_at CCDC142 NM_032779 873 242966_x_at — — 874 224229_s_at AKT3 NM_005465 875 218464_s_at C17orf63 NM_001077498 876 204924_at TLR2 NM_003264 877 228532_at C1orf162 NM_174896 878 1560060_s_at VPS37C NM_017966 879 1559282_at — — 880 217380_s_at XPNPEP1 NM_020383 881 223002_s_at XRN2 NM_012255 882 229145_at C10orf104 NM_173473 883 224911_s_at DCBLD2 NM_080927 884 205466_s_at HS3ST1 NM_005114 885 203907_s_at IQSEC1 NM_001134382 886 232645_at LOC153684 NR_015447 887 233575_s_at TLE4 NM_007005 888 200019_s_at FAU NM_001997 889 225738_at RAPGEF1 NM_005312 890 1561018_at — — 891 236460_at — — 892 217518_at MYOF NM_013451 893 212836_at POLD3 NM_006591 894 213264_at PCBP2 NM_001098620 895 1557852_at — — 896 202708_s_at HIST2H2BE NM_003528 897 231763_at POLR3A NM_007055 898 223259_at ORMDL3 NM_139280 899 213729_at PRPF40A NM_017892 900 219978_s_at NUSAP1 NM_001129897 901 222674_at C9orf114 NM_016390 902 223363_at PSMG3 NM_001134340 903 221188_s_at CIDEB NM_014430 904 223059_s_at FAM107B NM_031453 905 220557_s_at PACS1 NM_018026 906 219576_at MAP7D3 NM_024597 907 204326_x_at MT1X NM_005952 908 235575_at — — 909 201256_at COX7A2L NM_004718 910 226645_at KLF2 NM_016270 911 1559050_at HCG27 NR_026791 912 212114_at LOC552889 NM_001136262 913 209948_at KCNMB1 NM_004137 914 236407_at KCNE1 NM_000219 915 240481_at — — 916 208195_at TTN NM_003319 917 203728_at BAK1 NM_001188 918 227684_at S1PR2 NM_004230 919 236002_at — — 920 204270_at SKI NM_003036 921 202345_s_at FABP5 NM_001444 922 219745_at TMEM180 NM_024789 923 235802_at PLD4 NM_138790 924 230375_at SFRS18 NM_015491 925 219066_at PPCDC NM_021823 926 1560339_s_at NAP1L4 NM_005969 927 203110_at PTK2B NM_004103 928 219630_at PDZK1IP1 NM_005764 929 244307_s_at — — 930 238758_at — — 931 214917_at PRKAA1 NM_006251 932 218276_s_at SAV1 NM_021818 933 228193_s_at C13orf15 NM_014059 934 227037_at PLD6 NM_178836 935 225831_at LUZP1 NM_001142546 936 237210_at NFRKB NM_001143835 937 221206_at PMS2 NM_000535 938 221499_s_at STX16 NM_001001433 939 222217_s_at SLC27A3 NM_024330 940 238523_at KLHL36 NM_024731 941 202048_s_at CBX6 NM_014292 942 212856_at GRAMD4 NM_015124 943 225759_x_at CLMN NM_024734 944 217606_at — — 945 200973_s_at TSPAN3 NM_005724 946 230970_at — — 947 228932_at — — 948 209338_at TFCP2 NM_005653 949 205758_at CD8A NM_001145873 950 225065_x_at NCRNA00188 NM_152350 951 203358_s_at EZH2 NM_004456 952 209375_at XPC NM_001145769 953 232489_at CCDC76 NM_019083 954 239723_at — — 955 1552641_s_at ATAD3A NM_018188 956 1558220_at MUC20 NM_001098516 957 239960_x_at LYRM7 NM_181705 958 225212_at SLC25A25 NM_001006641 959 214815_at TRIM33 NM_015906 960 212135_s_at ATP2B4 NM_001001396 961 232952_at — — 962 1556053_at DNAJC7 NM_001144766 963 232346_at LOC388692 NR_027002 964 228967_at EIF1 NM_005801 965 239556_at LOC645513 XR_078309 966 226863_at FAM110C NM_001077710 967 229810_at — — 968 236669_at — — 969 222480_at UBE2Q1 NM_017582 970 228281_at C11orf82 NM_145018 971 216945_x_at PASK NM_015148 972 207434_s_at FXYD2 NM_001127489 973 201674_s_at AKAP1 NM_003488 974 239037_at GNAS NM_000516 975 1560434_x_at CLTA NM_001076677 976 244556_at LCP2 NM_005565 977 1552386_at GAPT NM_152687 978 222906_at FLVCR1 NM_014053 979 1554057_at LOC645676 NR_027023 980 204512_at HIVEP1 NM_002114 981 210512_s_at VEGFA NM_001025366 982 223816_at SLC46A2 NM_033051 983 239251_at — — 984 208998_at UCP2 NM_003355 985 213810_s_at LOC100292682 XM_002345924 986 207194_s_at ICAM4 NM_001039132 987 226372_at CHST11 NM_018413 988 201792_at AEBP1 NM_001129 989 221203_s_at YEATS2 NM_018023 990 1562019_at NT5DC4 XM_001715677 991 205613_at SYT17 NM_016524 992 221581_s_at LAT2 NM_014146 993 244834_at C1orf134 — 994 201557_at VAMP2 NM_014232 995 225549_at DDX6 NM_004397 996 201800_s_at OSBP NM_002556 997 202548_s_at ARHGEF7 NM_001113511 998 222243_s_at TOB2 NM_016272 999 2028_s_at E2F1 NM_005225 1000 223000_s_at F11R NM_016946 1001 222282_at — — 1002 230252_at LPAR5 NM_001142961 1003 227152_at C12orf35 NM_018169 1004 242877_at — — 1005 236908_at — — 1006 201365_at OAZ2 NM_002537 1007 241117_at LOXHD1 NM_001145472 1008 230663_at — — 1009 209579_s_at MBD4 NM_003925 1010 1562007_at — — 1011 222672_at LYRM4 NM_020408 1012 206842_at KCND1 NM_004979 1013 213034_at QSK NM_025164 1014 203764_at DLGAP5 NM_001146015 1015 239014_at CCAR1 NM_018237 1016 204029_at CELSR2 NM_001408 1017 204711_at KIAA0753 NM_014804 1018 221595_at — — 1019 1552671_a_at SLC9A7 NM_032591 1020 226066_at MITF NM_000248 1021 1553364_at PNPLA1 NM_001145716 1022 224653_at EIF4EBP2 NM_004096 1023 1564435_a_at KRT72 NM_001146225 1024 229253_at THEM4 NM_053055 1025 222044_at PCIF1 NM_022104 1026 241705_at ABCA5 NM_018672 1027 228469_at PPID NM_005038 1028 204858_s_at TYMP NM_001113755 1029 201621_at NBL1 NM_005380 1030 234492_at — — 1031 212906_at GRAMD1B NM_020716 1032 231116_at — — 1033 232782_at — — 1034 224978_s_at USP36 NM_025090 1035 225471_s_at AKT2 NM_001626 1036 1556496_a_at — — 1037 224717_s_at C19orf42 NM_024104 1038 241819_at TNFSF8 NM_001244 1039 202286_s_at TACSTD2 NM_002353 1040 219198_at GTF3C4 NM_012204 1041 202800_at SLC1A3 NM_004172 1042 208003_s_at NFAT5 NM_001113178 1043 36030_at IFFO1 NM_001039670 1044 219826_at ZNF419 NM_001098491 1045 214005_at GGCX NM_000821 1046 241669_x_at PRKD2 NM_001079880 1047 227122_at ZNF791 NM_153358 1048 205560_at PCSK5 NM_006200 1049 1564378_a_at — — 1050 240721_at — — 1051 228306_at CNIH4 NM_014184 1052 202074_s_at OPTN NM_001008211 1053 210559_s_at CDC2 NM_001130829 1054 206873_at CA6 NM_001215 1055 225221_at — — 1056 213180_s_at GOSR2 NM_001012511 1057 200622_x_at CALM3 NM_005184 1058 229413_s_at — — 1059 220466_at CCDC15 NM_025004 1060 229817_at ZNF608 NM_020747 1061 238417_at PGM2L1 NM_173582 1062 240845_at — — 1063 238016_s_at LOC100129105 — 1064 226765_at SPTBN1 NM_003128 1065 217856_at RBM8A NM_005105 1066 220865_s_at PDSS1 NM_014317 1067 232588_at STAG1 NM_005862 1068 201736_s_at MARCH6 NM_005885 1069 1561195_at — — 1070 236422_at — — 1071 209118_s_at TUBA1A NM_006009 1072 229622_at FAM132B XM_001130886 1073 227490_at WDFY2 NM_052950 1074 226517_at BCAT1 NM_005504 1075 230405_at C5orf56 NM_001013717 1076 201543_s_at SAR1A NM_001142648 1077 200855_at C20orf191 NM_006311 1078 209193_at PIM1 NM_002648 1079 216857_at — — 1080 225207_at PDK4 NM_002612 1081 203969_at PEX3 NM_003630 1082 243444_at SRD5A3 NM_024592 1083 227091_at CCDC146 NM_020879 1084 1007_s_at DDR1 NM_001954 1085 205493_s_at DPYSL4 NM_006426 1086 208304_at CCR3 NM_001837 1087 210470_x_at NONO NM_001145408 1088 210947_s_at MSH3 NM_002439 1089 218561_s_at LYRM4 NM_020408 1090 208613_s_at FLNB NM_001164317 1091 227436_at OTUD7B NM_020205 1092 242729_at — — 1093 237387_at — — 1094 206005_s_at KIAA1009 NM_014895 1095 218807_at VAV3 NM_001079874 1096 219371_s_at KLF2 NM_016270 1097 243858_at — — 1098 219412_at RAB38 NM_022337 1099 233224_at — — 1100 226571_s_at PTPRS NM_002850 1101 212828_at SYNJ2 NM_003898 1102 239212_at LTV1 NM_032860 1103 241763_s_at FBXO32 NM_058229 1104 1570566_at LOC100292875 XM_002345815 1105 1554985_at ZNF396 NM_145756 1106 202291_s_at MGP NM_000900 1107 235308_at ZBTB20 NM_015642 1108 219433_at BCOR NM_001123383 1109 230023_at NSUN4 NM_199044 1110 204578_at HISPPD2A NM_001024463 1111 212599_at AUTS2 NM_001127231 1112 236836_at — — 1113 234088_at — — 1114 222667_s_at ASH1L NM_018489 1115 231221_at CLEC16A NM_015226 1116 202449_s_at RXRA NM_002957 1117 209455_at FBXW11 NM_012300 1118 1554237_at SDCCAG8 NM_006642 1119 1556352_at — — 1120 205844_at VNN1 NM_004666 1121 1562453_at — — 1122 1552613_s_at CDC42SE2 NM_001038702 1123 225204_at PPTC7 NM_139283 1124 209806_at HIST1H2BK NM_080593 1125 240154_at — — 1126 239464_at — — 1127 1555370_a_at CAMTA1 NM_015215 1128 225004_at TMEM101 NM_032376 1129 226893_at ABL2 NM_001100108 1130 202190_at CSTF1 NM_001033521 1131 202369_s_at TRAM2 NM_012288 1132 243973_at — — 1133 242862_x_at — — 1134 236327_at — — 1135 235778_s_at ANKRD44 NM_153697 1136 229686_at P2RY8 NM_178129 1137 213924_at MPPE1 NM_023075 1138 239792_at LOC440288 XM_001714156 1139 206560_s_at MIA NM_006533 1140 225654_at NSD1 NM_022455 1141 228570_at BTBD11 NM_001017523 1142 231064_s_at — — 1143 238126_at — — 1144 212995_x_at FAM128B NM_025029 1145 214617_at PRF1 NM_001083116 1146 208269_s_at ADAM28 NM_014265 1147 1568717_a_at FKBP15 NM_015258 1148 1558854_a_at — — 1149 241938_at QKI NM_006775 1150 215099_s_at RXRB NM_021976 1151 230100_x_at PAK1 NM_001128620 1152 202464_s_at PFKFB3 NM_001145443 1153 225454_at CCDC124 NM_001136203 1154 1558201_s_at SLC4A1AP NM_018158 1155 239597_at — — 1156 228772_at HNMT NM_001024074 1157 227722_at RPS23 NM_001025 1158 243340_at — — 1159 217769_s_at POMP NM_015932 1160 228308_at FKBP11 NM_001143781 1161 204233_s_at CHKA NM_001277 1162 204440_at CD83 NM_001040280 1163 204184_s_at ADRBK2 NM_005160 1164 212099_at RHOB NM_004040 1165 212504_at DIP2C NM_014974 1166 220266_s_at KLF4 NM_004235 1167 227750_at KALRN NM_001024660 1168 203157_s_at GLS NM_014905 1169 202804_at ABCC1 NM_004996 1170 242752_at — — 1171 219799_s_at DHRS9 NM_001142270 1172 236191_at — — 1173 230725_at — — 1174 212137_at LARP1 NM_015315 1175 238135_at AGTRAP NM_001040194 1176 207079_s_at MED6 NM_005466 1177 203973_s_at CEBPD NM_005195 1178 228778_at — — 1179 240798_at — — 1180 244347_at — — 1181 231093_at FCRL3 NM_052939 1182 231435_at C7orf34 NM_178829 1183 244280_at — — 1184 224549_x_at — — 1185 228516_at CDAN1 NM_138477 1186 233218_at — — 1187 1560297_at — — 1188 227114_at RNF214 NM_001077239 1189 204373_s_at CEP350 NM_014810 1190 226190_at MAP3K13 NM_004721 1191 224564_s_at RTN3 NM_006054 1192 214483_s_at ARFIP1 NM_001025593 1193 209248_at GHITM NM_014394 1194 204576_s_at CLUAP1 NM_015041 1195 209467_s_at MKNK1 NM_001135553 1196 242970_at DIP2B NM_173602 1197 230233_at — — 1198 212560_at SORL1 NM_003105 1199 228786_at LOC387647 NR_003930 1200 211368_s_at CASP1 NM_001223 1201 202958_at PTPN9 NM_002833 1202 244262_x_at — — 1203 244647_at — — 1204 223457_at COPG2 NM_012133 1205 219260_s_at C17orf81 NM_015362 1206 236832_at LOC221442 NR_026938 1207 1552307_a_at TTC39C NM_001135993 1208 201336_at VAMP3 NM_004781 1209 AFFX-HUMISGF3A/ STAT1 NM_007315 M97935_MB_at 1210 227080_at ZNF697 NM_001080470 1211 227486_at NT5E NM_002526 1212 206413_s_at TCL1B NM_004918 1213 237120_at KRT77 NM_175078 1214 207091_at P2RX7 NM_002562 1215 226815_at C3orf31 NM_138807 1216 219025_at CD248 NM_020404 1217 227182_at SUSD3 NM_145006 1218 214877_at CDKAL1 NM_017774 1219 218997_at POLR1E NM_022490 1220 240861_at — — 1221 204592_at DLG4 NM_001128827 1222 213406_at WSB1 NM_015626 1223 227707_at MYLIP NM_013262 1224 237538_at — — 1225 218262_at RMND5B NM_022762 1226 232283_at LYSMD1 NM_001136543 1227 212249_at PIK3R1 NM_181504 1228 213672_at MARS NM_004990 1229 210052_s_at TPX2 NM_012112 1230 229893_at FRMD3 NM_174938 1231 222482_at LOC100129321 NM_001009955 1232 203129_s_at KIF5C NM_004522 1233 203418_at CCNA2 NM_001237 1234 225091_at ZCCHC3 NM_033089 1235 240718_at LRMP NM_006152 1236 81737_at — — 1237 209360_s_at RUNX1 NM_001001890 1238 210912_x_at GSTM4 NM_000850 1239 203716_s_at DPP4 NM_001935 1240 225095_at SPTLC2 NM_004863 1241 213048_s_at — — 1242 40016_g_at MAST4 NM_015183 1243 224965_at GNG2 NM_053064 1244 207623_at ABCF2 NM_005692 1245 226171_at ZDHHC3 NM_001135179 1246 241242_at — — 1247 207535_s_at NFKB2 NM_001077493 1248 239522_at IL12RB1 NM_005535 1249 215191_at — — 1250 212161_at AP2A2 NM_012305 1251 1562280_at — — 1252 209122_at PLIN2 NM_001122 1253 229305_at MLF1IP NM_024629 1254 210407_at PPM1A NM_021003 1255 226614_s_at FAM167A NM_053279 1256 1555679_a_at RTN4IP1 NM_032730 1257 224697_at DCAF5 NM_003861 1258 222262_s_at ETNK1 NM_001039481 1259 227331_at ZNF740 NM_001004304 1260 227259_at CD47 NM_001025079 1261 222000_at C1orf174 NM_207356 1262 205603_s_at DIAPH2 NM_006729 1263 213021_at GOSR1 NM_001007024 1264 210149_s_at ATP5H NM_001003785 1265 240793_at TTN NM_003319 1266 200089_s_at RPL4 NM_000968 1267 217834_s_at SYNCRIP NM_001159673 1268 234396_at — — 1269 1566001_at — — 1270 236742_at — — 1271 240231_at — — 1272 200664_s_at DNAJB1 NM_006145 1273 212671_s_at HLA-DQA1 NM_002122 1274 207536_s_at TNFRSF9 NM_001561 1275 232218_at — — 1276 205545_x_at DNAJC8 NM_014280 1277 239654_at CHD9 NM_025134 1278 223907_s_at PINX1 NM_017884 1279 233483_at TBC1D27 XM_002343481 1280 1556643_at LOC100128718 — 1281 209502_s_at BAIAP2 NM_001144888 1282 232720_at LINGO2 NM_152570 1283 227138_at CRTAP NM_006371 1284 1559895_x_at — — 1285 203554_x_at PTTG1 NM_004219 1286 220305_at MAVS NM_020746 1287 229966_at EWSR1 NM_001163285 1288 230435_at LOC375190 NM_001145710 1289 220000_at SIGLEC5 NM_003830 1290 1558094_s_at C3orf19 NM_016474 1291 233157_x_at CCDC114 NM_144577 1292 205067_at IL1B NM_000576 1293 212662_at PVR NM_001135768 1294 213028_at NFRKB NM_001143835 1295 224836_at TP53INP2 NM_021202 1296 210648_x_at SNX3 NM_003795 1297 1558105_a_at — — 1298 210153_s_at ME2 NM_002396 1299 212650_at EHBP1 NM_001142614 1300 209281_s_at ATP2B1 NM_001001323 1301 239307_at — — 1302 213902_at ASAH1 NM_001127505 1303 1559835_at — — 1304 240070_at TIGIT NM_173799 1305 222396_at HN1 NM_001002032 1306 211781_x_at — — 1307 219179_at DACT1 NM_001079520 1308 207072_at IL18RAP NM_003853 1309 224919_at MRPS6 NM_032476 1310 231547_at ZBTB37 NM_001122770 1311 227476_at LPGAT1 NM_014873 1312 218584_at TCTN1 NM_001082537 1313 209714_s_at CDKN3 NM_001130851 1314 230354_at — — 1315 228831_s_at GNG7 NM_052847 1316 237522_at FAS NM_000043 1317 212663_at FKBP15 NM_015258 1318 226336_at PPIA NM_021130 1319 242673_at — — 1320 232784_at — — 1321 205232_s_at PAFAH2 NM_000437 1322 201364_s_at OAZ2 NM_002537 1323 1558143_a_at BCL2L11 NM_006538 1324 1569906_s_at PHF20 NM_016436 1325 244332_at — — 1326 242082_at MMAB NM_052845 1327 212154_at SDC2 NM_002998 1328 1569207_s_at TCP11L2 NM_152772 1329 232257_s_at — — 1330 239060_at — — 1331 1563075_s_at — — 1332 208645_s_at RPS14 NM_001025070 1333 244035_at — — 1334 227402_s_at UTP23 NM_032334 1335 230921_s_at — — 1336 227790_at UBE2CBP NM_198920 1337 242582_at — — 1338 1553405_a_at CSMD1 NM_033225 1339 244886_at LOC389641 XM_374260 1340 228762_at LFNG NM_001040167 1341 220231_at C7orf16 NM_001145123 1342 202341_s_at TRIM2 NM_001130067 1343 208054_at HERC4 NM_015601 1344 228003_at RAB30 NM_014488 1345 243534_at CC2D2B NM_001001732 1346 212573_at ENDOD1 NM_015036 1347 230063_at ZNF264 NM_003417 1348 204165_at WASF1 NM_001024934 1349 238279_x_at — — 1350 201842_s_at EFEMP1 NM_001039348 1351 223132_s_at TRIM8 NM_030912 1352 244043_at — — 1353 206043_s_at ATP2C2 NM_014861 1354 208653_s_at CD164 NM_001142401 1355 238596_at C10orf4 NM_145246 1356 231907_at ABL2 NM_001100108 1357 1557478_at — — 1358 1558906_a_at — — 1359 243404_at — — 1360 243526_at WDR86 NM_198285 1361 205531_s_at GLS2 NM_013267 1362 241702_at — — 1363 223821_s_at SUSD4 NM_001037175 1364 242232_at — — 1365 221573_at C7orf25 NM_001099858 1366 1569492_at — — 1367 227897_at — — 1368 231809_x_at PDCD7 NM_005707 1369 226982_at ELL2 NM_012081 1370 214876_s_at TUBGCP5 NM_001102610 1371 226957_x_at RALBP1 NM_006788 1372 204251_s_at CEP164 NM_014956 1373 229881_at KLF12 NM_007249 1374 205039_s_at IKZF1 NM_006060 1375 214000_s_at RGS10 NM_001005339 1376 204454_at LDOC1 NM_012317 1377 229821_at — — 1378 202166_s_at PPP1R2 NM_006241 1379 228177_at CREBBP NM_001079846 1380 233063_s_at — — 1381 204173_at MYL6B NM_002475 1382 206513_at AIM2 NM_004833 1383 218949_s_at QRSL1 NM_018292 1384 204167_at BTD NM_000060 1385 221256_s_at HDHD3 NM_031219 1386 220239_at KLHL7 NM_001031710 1387 201307_at 40787 NM_018243 1388 232530_at PLD1 NM_001130081 1389 226519_s_at AGXT2L2 NM_153373 1390 219298_at ECHDC3 NM_024693 1391 209134_s_at RPS6 NM_001010 1392 219529_at CLIC3 NM_004669 1393 202759_s_at AKAP2 NM_001004065 1394 232829_at OR52K3P — 1395 1559391_s_at — — 1396 203605_at SRP54 NM_001146282 1397 232636_at SLITRK4 NM_173078 1398 201061_s_at STOM NM_004099 1399 222633_at TBL1XR1 NM_024665 1400 214710_s_at CCNB1 NM_031966 1401 231866_at LNPEP NM_005575 1402 208619_at DDB1 NM_001923 1403 1566039_a_at — — 1404 202891_at NIT1 NM_005600 1405 229334_at RUFY3 NM_001037442 1406 214686_at ZNF266 NM_006631 1407 1565358_at RARA NM_000964 1408 243924_at LOC100127980 XM_001720119 1409 225007_at — — 1410 213285_at TMEM30B NM_001017970 1411 206592_s_at AP3D1 NM_001077523 1412 221909_at RNFT2 NM_001109903 1413 218746_at TAPBPL NM_018009 1414 235927_at XPO1 NM_003400 1415 240089_at — — 1416 1554292_a_at UHRF1BP1L NM_001006947 1417 208636_at ACTN1 NM_001102 1418 206342_x_at IDS NM_000202 1419 217374_x_at TARP NM_001003799 1420 223527_s_at CDADC1 NM_030911 1421 209686_at S100B NM_006272 1422 242364_x_at LOC100131096 XM_001720907 1423 201523_x_at UBE2N NM_003348 1424 202348_s_at TOR1A NM_000113 1425 1558847_at — — 1426 220363_s_at ELMO2 NM_133171 1427 234860_at — — 1428 232095_at — — 1429 211984_at CALM1 NM_006888 1430 201053_s_at PSMF1 NM_006814 1431 207332_s_at TFRC NM_001128148 1432 235942_at LOC401629 NR_002160 1433 218361_at GOLPH3L NM_018178 1434 205757_at ENTPD5 NM_001249 1435 220606_s_at C17orf48 NM_020233 1436 209642_at BUB1 NM_004336 1437 203264_s_at ARHGEF9 NM_015185 1438 236193_at HIST1H2BC NM_003526 1439 229274_at GNAS NM_000516 1440 225661_at IFNAR1 NM_000629 1441 202070_s_at IDH3A NM_005530 1442 201286_at SDC1 NM_001006946 1443 237229_at JMJD5 NM_001145348 1444 221776_s_at BRD7 NM_013263 1445 218312_s_at ZSCAN18 NM_001145542 1446 230444_at — — 1447 205708_s_at TRPM2 NM_003307 1448 224794_s_at CERCAM NM_016174 1449 240237_at — — 1450 225834_at FAM72A NM_001100910 1451 218064_s_at AKAP8L NM_014371 1452 229592_at — — 1453 209445_x_at C7orf44 NM_018224 1454 208517_x_at BTF3 NM_001037637 1455 217677_at PLEKHA2 NM_021623 1456 210812_at XRCC4 NM_003401 1457 224756_s_at BAT5 NM_021160 1458 1559059_s_at ZNF611 NM_001161499 1459 225609_at GSR NM_000637 1460 242691_at — — 1461 204785_x_at IFNAR2 NM_000874 1462 233191_at RUFY2 NM_001042417 1463 225987_at STEAP4 NM_024636 1464 212010_s_at CDV3 NM_001134422 1465 229398_at RAB18 NM_021252 1466 234819_at — — 1467 230440_at ZNF469 NM_001127464 1468 1557186_s_at TPCN1 NM_001143819 1469 222651_s_at TRPS1 NM_014112 1470 212881_at PIAS4 NM_015897 1471 226546_at — — 1472 243201_at HNRNPH2 NM_001032393 1473 204254_s_at VDR NM_000376 1474 244070_at SYNE1 NM_015293 1475 210622_x_at CDK10 NM_001098533 1476 217620_s_at PIK3CB NM_006219 1477 238573_at — — 1478 201777_s_at KIAA0494 NM_014774 1479 231598_x_at — — 1480 202055_at KPNA1 NM_002264 1481 226856_at MUSTN1 NM_205853 1482 1553858_at ZBTB3 NM_024784 1483 235446_at — — 1484 242064_at SDK2 NM_001144952 1485 239574_at — — 1486 204069_at MEIS1 NM_002398 1487 212390_at PDE4DIP NM_001002810 1488 AFFX- — — hum_alu_at 1489 212933_x_at RPL13 NM_000977 1490 212547_at BRD3 NM_007371 1491 241202_at — — 1492 1562067_at — — 1493 1566427_at — — 1494 211717_at ANKRD40 NM_052855 1495 1552485_at LACTB NM_032857 1496 209345_s_at PI4K2A NM_018425 1497 232722_at RNASET2 NM_003730 1498 225755_at KLHDC8B NM_173546 1499 209541_at IGF1 NM_000618 1500 230669_at RASA2 NM_006506 1501 217207_s_at BTNL3 NM_197975 1502 230442_at MTHFSD NM_001159377 1503 200983_x_at CD59 NM_000611 1504 217232_x_at HBB NM_000518 1505 202738_s_at PHKB NM_000293 1506 218773_s_at MSRB2 NM_012228 1507 213343_s_at GDPD5 NM_030792 1508 233880_at RNF213 NM_020914 1509 214377_s_at CTRL NM_001907 1510 211324_s_at RGPD5 NM_001037866 1511 219055_at SRBD1 NM_018079 1512 237389_at — — 1513 225729_at C6orf89 NM_152734 1514 221521_s_at GINS2 NM_016095 1515 1559449_a_at ZNF254 NM_203282 1516 1565674_at FCGR2A NM_001136219 1517 241933_at QRSL1 NM_018292 1518 215559_at ABCC6 NM_001079528 1519 221928_at ACACB NM_001093 1520 226051_at SELM NM_080430 1521 1566166_at — — 1522 225208_s_at FAM103A1 NM_031452 1523 243675_at — — 1524 239944_at — — 1525 58780_s_at FLJ10357 NM_018071 1526 235030_at FAM55C NM_001134456 1527 239783_at — — 1528 1555580_at — — 1529 238494_at TRAF3IP1 NM_001139490 1530 1558719_s_at RPAIN NM_001033002 1531 41660_at CELSR1 NM_014246 1532 210971_s_at ARNTL NM_001030272 1533 201825_s_at SCCPDH NM_016002 1534 217727_x_at VPS35 NM_018206 1535 203725_at GADD45A NM_001924 1536 225490_at ARID2 NM_152641 1537 231023_at CARS2 NM_024537 1538 224524_s_at ASB3 NM_001164165 1539 232959_at — — 1540 237133_at — — 1541 218391_at SNF8 NM_007241 1542 227060_at RELT NM_032871 1543 200606_at DSP NM_001008844 1544 219681_s_at RAB11FIP1 NM_001002233 1545 225638_at C1orf31 NM_001012985 1546 227957_at GSN NM_000177 1547 239560_at — — 1548 208936_x_at LGALS8 NM_006499 1549 226334_s_at AHSA2 NM_152392 1550 238750_at — — 1551 225061_at DNAJA4 NM_001130182 1552 228096_at C1orf151 NM_001032363 1553 209709_s_at HMMR NM_001142556 1554 207277_at CD209 NM_001144893 1555 212448_at NEDD4L NM_001144964 1556 229734_at LOC283174 NR_024344 1557 237332_at — — 1558 228069_at FAM54A NM_001099286 1559 1553978_at LOC729991 NM_001145783 1560 205042_at GNE NM_001128227 1561 229820_at LOC440993 XR_039810 1562 1554513_s_at CCDC123 NM_032816 1563 221877_at IRGQ NM_001007561 1564 208696_at CCT5 NM_012073 1565 240554_at AKAP8L NM_014371 1566 201589_at SMC1A NM_006306 1567 235824_at — — 1568 232375_at — — 1569 242384_at — — 1570 203155_at SETDB1 NM_001145415 1571 243092_at LOC100290367 XM_002347376 1572 232045_at PHACTR1 NM_030948 1573 1554710_at KCNMB1 NM_004137 1574 237104_at — — 1575 231223_at CSMD1 NM_033225 1576 1553920_at C9orf84 NM_001080551 1577 225901_at PTPMT1 NM_001143984 1578 212975_at DENND3 NM_014957 1579 1554365_a_at PPP2R5C NM_001161725 1580 215495_s_at SAMD4A NM_001161576 1581 206521_s_at GTF2A1 NM_015859 1582 215032_at RREB1 NM_001003698 1583 226879_at HVCN1 NM_001040107 1584 221582_at HIST3H2A NM_033445 1585 225276_at GSPT1 NM_001130006 1586 1560520_at LOC401312 — 1587 214997_at SCAI NM_001144877 1588 220393_at LGSN NM_001143940 1589 230952_at LOC730092 NR_003370 1590 242279_at — — 1591 222592_s_at ACSL5 NM_016234 1592 209426_s_at AMACR NM_014324 1593 231099_at FKBP15 NM_015258 1594 206860_s_at MIOS NM_019005 1595 208628_s_at YBX1 NM_004559 1596 1566447_at CD6 NM_006725 1597 236298_at PDSS1 NM_014317 1598 237426_at SP100 NM_001080391 1599 230000_at RNF213 NM_020914 1600 221815_at ABHD2 NM_007011 1601 234747_at C4orf41 NM_021942 1602 236149_at — — 1603 209357_at CITED2 NM_006079 1604 225111_s_at NAPB NM_022080 1605 226270_at EXOC2 NM_018303 1606 210858_x_at ATM NM_000051 1607 212320_at TUBB NM_178014 1608 214464_at CDC42BPA NM_003607 1609 205619_s_at MEOX1 NM_001040002 1610 225606_at BCL2L11 NM_006538 1611 217774_s_at TRMT112 NM_016404 1612 228219_s_at UPB1 NM_016327 1613 243958_at — — 1614 208503_s_at GATAD1 NM_021167 1615 227170_at ZNF316 XR_078353 1616 201698_s_at SFRS9 NM_003769 1617 239698_at — — 1618 1555904_at — — 1619 217825_s_at UBE2J1 NM_016021 1620 240247_at — — 1621 218108_at UBR7 NM_001100417 1622 1559688_at LOC400581 NM_001129778 1623 218125_s_at CCDC25 NM_018246 1624 226666_at DAAM1 NM_014992 1625 230027_s_at MRPL43 NM_032112 1626 205227_at IL1RAP NM_002182 1627 228513_at TMEM219 NM_001083613 1628 1552677_a_at DIP2A NM_001146114 1629 215796_at — — 1630 201903_at UQCRC1 NM_003365 1631 1560869_a_at — — 1632 234852_at — — 1633 227708_at EEF1A1 NM_001402 1634 1552807_a_at SIGLEC10 NM_033130 1635 229844_at — — 1636 204992_s_at PFN2 NM_002628 1637 227613_at ZNF331 NM_001079906 1638 243579_at MSI2 NM_138962 1639 210146_x_at LILRB2 NM_001080978 1640 202433_at SLC35B1 NM_005827 1641 209870_s_at APBA2 NM_001130414 1642 224698_at ESYT2 NM_020728 1643 207617_at DDX3X NM_001356 1644 203445_s_at CTDSP2 NM_005730 1645 235761_at — — 1646 238546_at SLC8A1 NM_001112800 1647 1554536_at DPYD NM_000110 1648 200701_at NPC2 NM_006432 1649 1558722_at ZNF252 NR_023392 1650 239033_at — — 1651 201003_x_at RNPEP NM_001032288 1652 228984_at ATPGD1 NM_020811 1653 217025_s_at DBN1 NM_004395 1654 232826_at — — 1655 241692_at — — 1656 205826_at MYOM2 NM_003970 1657 242079_at RGS12 NM_002926 1658 242014_at — — 1659 205612_at MMRN1 NM_007351 1660 225546_at EEF2K NM_013302 1661 1559284_at — — 1662 202583_s_at RANBP9 NM_005493 1663 225673_at MYADM NM_001020818 1664 1556072_at C22orf37 XR_040827 1665 201190_s_at PITPNA NM_006224 1666 218751_s_at FBXW7 NM_001013415 1667 209069_s_at H3F3B NM_005324 1668 210285_x_at WTAP NM_004906 1669 209131_s_at SNAP23 NM_003825 1670 222491_at HGSNAT NM_152419 1671 213198_at ACVR1B NM_004302 1672 233006_at 38231 NM_004574 1673 1555408_at BAGE2 NM_181704 1674 243558_at — — 1675 228373_at C16orf72 NM_014117 1676 231578_at GBP1 NM_002053 1677 241233_x_at C21orf81 NR_027270 1678 213851_at TMEM110 NM_198563 1679 219269_at HMBOX1 NM_001135726 1680 222276_at — — 1681 217741_s_at ZFAND5 NM_001102420 1682 202080_s_at TRAK1 NM_001042646 1683 224406_s_at FCRL5 NM_031281 1684 220987_s_at C11orf17 NM_020642 1685 230618_s_at — — 1686 213274_s_at CTSB NM_001908 1687 229202_at — — 1688 237009_at — — 1689 228125_at ZNF397OS NM_001112734 1690 225820_at PHF17 NM_024900 1691 218348_s_at ZC3H7A NM_014153 1692 232883_at — — 1693 209006_s_at C1orf63 NM_020317 1694 211269_s_at IL2RA NM_000417 1695 240415_at — — 1696 231219_at CMTM1 NM_052999 1697 226931_at TMTC1 NM_175861 1698 243720_at CMIP NM_030629 1699 208780_x_at VAPA NM_003574 1700 213788_s_at NCRNA00094 NR_015427 1701 200806_s_at HSPD1 NM_002156 1702 217810_x_at LARS NM_020117 1703 219489_s_at NXN NM_022463 1704 227201_at LOC643837 NR_015368 1705 202732_at PKIG NM_007066 1706 206777_s_at CRYBB2 NM_000496 1707 1568680_s_at YTHDC2 NM_022828 1708 224163_s_at DMAP1 NM_001034023 1709 1559658_at C15orf29 NM_024713 1710 228645_at SNHG9 NR_003142 1711 238946_at — — 1712 202898_at SDC3 NM_014654 1713 1566485_at — — 1714 1554112_a_at ULK2 NM_001142610 1715 214583_at RSC1A1 NM_006511 1716 1556202_at SRGAP2 NM_001042758 1717 212169_at FKBP9 NM_007270 1718 200950_at ARPC1A NM_006409 1719 201009_s_at TXNIP NM_006472 1720 201060_x_at STOM NM_004099 1721 244633_at — — 1722 231973_s_at ANAPC1 NM_022662 1723 215013_s_at USP34 NM_014709 1724 237461_at NLRP7 NM_001127255 1725 227793_at — — 1726 238952_x_at ZNF829 NM_001037232 1727 228806_at RORC NM_001001523 1728 204826_at CCNF NM_001761 1729 214132_at ATP5C1 NM_001001973 1730 213119_at SLC36A1 NM_078483 1731 1558795_at LOC728052 XM_001717850 1732 222760_at ZNF703 NM_025069 1733 211742_s_at EVI2B NM_006495 1734 243356_at FAM7A3 NR_026859 1735 203063_at PPM1F NM_014634 1736 207922_s_at MAEA NM_001017405 1737 228314_at LRRC8C NM_032270 1738 214137_at PTPRJ NM_001098503 1739 216234_s_at PRKACA NM_002730 1740 242551_at — — 1741 207610_s_at EMR2 NM_013447 1742 1559205_s_at — — 1743 217292_at MTMR7 NM_004686 1744 231055_at — — 1745 217984_at RNASET2 NM_003730 1746 216370_s_at TKTL1 NM_001145933 1747 37433_at PIAS2 NM_004671 1748 201141_at GPNMB NM_001005340 1749 226816_s_at KIAA1143 NM_020696 1750 239175_at — — 1751 209158_s_at CYTH2 NM_004228 1752 203588_s_at TFDP2 NM_006286 1753 212823_s_at PLEKHG3 NM_015549 1754 202822_at LPP NM_005578 1755 1561058_at — — 1756 225802_at TOP1MT NM_052963 1757 213931_at ID2 NM_002166 1758 223666_at SNX5 NM_014426 1759 201827_at SMARCD2 NM_001098426 1760 235052_at ZNF792 NM_175872 1761 227579_at — — 1762 228105_at — — 1763 236292_at RNF130 NM_018434 1764 219550_at ROBO3 NM_022370 1765 242216_at — — 1766 229544_at — — 1767 227533_at — — 1768 209442_x_at ANK3 NM_001149 1769 37145_at GNLY NM_006433 1770 225680_at LRWD1 NM_152892 1771 209239_at NFKB1 NM_003998 1772 213579_s_at EP300 NM_001429 1773 213073_at ZFYVE26 NM_015346 1774 206855_s_at HYAL2 NM_003773 1775 205277_at PRDM2 NM_001007257 1776 214076_at GFOD2 NM_030819 1777 1558167_a_at MGC16275 NR_026914 1778 1568574_x_at SPP1 NM_000582 1779 224298_s_at UBAC2 NM_001144072 1780 230795_at — — 1781 221765_at UGCG NM_003358 1782 201668_x_at MARCKS NM_002356 1783 1556432_at — — 1784 227203_at FBXL17 NM_001163315 1785 208743_s_at YWHAB NM_003404 1786 212008_at UBXN4 NM_014607 1787 220072_at CSPP1 NM_001077204 1788 1563467_at — — 1789 224639_at UNQ1887 NM_139015 1790 230480_at PIWIL4 NM_152431 1791 1562013_a_at — — 1792 204909_at DDX6 NM_004397 1793 209824_s_at ARNTL NM_001030272 1794 206409_at TIAM1 NM_003253 1795 208174_x_at ZRSR2 NM_005089 1796 211015_s_at HSPA4 NM_002154 1797 208658_at PDIA4 NM_004911 1798 213801_x_at RPSA NM_001012321 1799 225634_at ZC3HAV1 NM_020119 1800 215356_at TDRD12 NM_001110822 1801 213688_at CALM1 NM_006888 1802 200933_x_at RPS4X NM_001007 1803 242270_at — — 1804 210036_s_at KCNH2 NM_000238 1805 229822_at — — 1806 232739_at SPIB NM_003121 1807 201219_at CTBP2 NM_001083914 1808 1560457_x_at LSDP5 NM_001013706 1809 1558372_at — — 1810 1568851_at — — 1811 225849_s_at SFT2D1 NM_145169 1812 1557257_at BCL10 NM_003921 1813 231283_at MGAT4A NM_001160154 1814 239154_at — — 1815 212523_s_at KIAA0146 NM_001080394 1816 231922_at ZNF276 NM_001113525 1817 213514_s_at DIAPH1 NM_001079812 1818 205129_at NPM3 NM_006993 1819 230997_at TTC21A NM_001105513 1820 208705_s_at EIF5 NM_001969 1821 203939_at NT5E NM_002526 1822 217770_at PIGT NM_015937 1823 233787_at C6orf163 NM_001010868 1824 241621_at SMCHD1 NM_015295 1825 1553750_a_at FAM76B NM_144664 1826 211018_at LSS NM_001001438 1827 200693_at YWHAQ NM_006826 1828 218662_s_at NCAPG NM_022346 1829 220990_s_at MIR21 NM_030938 1830 232149_s_at NSMAF NM_001144772 1831 232012_at CAPN1 NM_005186 1832 1562825_at — — 1833 233800_at — — 1834 242806_at — — 1835 1557544_at CCDC147 NM_001008723 1836 203923_s_at CYBB NM_000397 1837 217978_s_at UBE2Q1 NM_017582 1838 220940_at ANKRD36B NM_025190 1839 221711_s_at C19orf62 NM_001033549 1840 222087_at PVT1 NR_003367 1841 213201_s_at TNNT1 NM_001126132 1842 219178_at QTRTD1 NM_024638 1843 213535_s_at UBE2I NM_003345 1844 204039_at CEBPA NM_004364 1845 215754_at SCARB2 NM_005506 1846 232284_at PSMD6 NM_014814 1847 204214_s_at RAB32 NM_006834 1848 219765_at ZNF329 NM_024620 1849 201938_at CDK2AP1 NM_004642 1850 230009_at FAM118B NM_024556 1851 239427_at — — 1852 214717_at DKFZp434H1419 — 1853 239755_at — — 1854 208322_s_at ST3GAL1 NM_003033 1855 227910_at XPNPEP3 NM_022098 1856 225382_at ZNF275 NM_001080485 1857 220167_s_at TP53TG3 NM_001099687 1858 1561884_at CEPT1 NM_001007794 1859 206332_s_at IFI16 NM_005531 1860 220193_at C1orf113 NM_001162530 1861 237623_at CST3 NM_000099 1862 209410_s_at GRB10 NM_001001549 1863 214481_at HIST1H2AM NM_003514 1864 236511_at — — 1865 226184_at FMNL2 NM_052905 1866 222955_s_at FAM45A NM_207009 1867 226840_at H2AFY NM_001040158 1868 227931_at INO80D NM_017759 1869 231837_at USP28 NM_020886 1870 232180_at UGP2 NM_001001521 1871 227305_s_at SMCR8 NM_144775 1872 237597_at — — 1873 1555963_x_at B3GNT7 NM_145236 1874 211874_s_at MYST4 NM_012330 1875 208541_x_at TFAM NM_003201 1876 242868_at — — 1877 227522_at CMBL NM_138809 1878 207324_s_at DSC1 NM_004948 1879 1560109_s_at — — 1880 231644_at — — 1881 1554360_at FCHSD2 NM_014824 1882 239988_at — — 1883 201898_s_at UBE2A NM_003336 1884 1555420_a_at KLF7 NM_003709 1885 209691_s_at DOK4 NM_018110 1886 213596_at CASP4 NM_001225 1887 243767_at — — 1888 218014_at NUP85 NM_024844 1889 208179_x_at KIR2DL3 NM_014511 1890 230761_at — — 1891 217100_s_at UBXN7 NM_015562 1892 242066_at — — 1893 232927_at — — 1894 217933_s_at LAP3 NM_015907 1895 205929_at GPA33 NM_005814 1896 234049_at hCG_1990547 NR_024361 1897 225435_at SSR1 NM_003144 1898 203560_at GGH NM_003878 1899 1555427_s_at SYNCRIP NM_001159673 1900 215127_s_at RBMS1 NM_002897 1901 1561211_at — — 1902 226509_at ZNF641 NM_152320 1903 228088_at SESTD1 NM_178123 1904 218126_at FAM82A2 NM_018145 1905 1559052_s_at PAK2 NM_002577 1906 203311_s_at ARF6 NM_001663 1907 232646_at TTC17 NM_018259 1908 218907_s_at LRRC61 NM_001142928 1909 232744_x_at — — 1910 213310_at EIF2C2 NM_012154 1911 222900_at NRIP3 NM_020645 1912 1552607_at NCRNA00204 NR_027401 1913 232237_at MDGA1 NM_153487 1914 1558310_s_at hCG_2039148 XR_078315 1915 233794_at — — 1916 207276_at CDR1 NM_004065 1917 228719_at ZSWIM7 NM_001042697 1918 200076_s_at C19orf50 NM_024069 1919 209750_at NR1D2 NM_001145425 1920 243033_at TWF1 NM_002822 1921 226210_s_at MEG3 NR_002766 1922 228434_at BTNL9 NM_152547 1923 204497_at ADCY9 NM_001116 1924 203339_at SLC25A12 NM_003705 1925 229980_s_at SNX5 NM_014426 1926 213531_s_at RAB3GAP1 NM_012233 1927 201487_at CTSC NM_001114173 1928 215169_at SLC35E2 NM_182838 1929 203342_at TIMM17B NM_005834 1930 222879_s_at POLH NM_006502 1931 239866_at — — 1932 209110_s_at RGL2 NM_004761 1933 214083_at PPP2R5C NM_001161725 1934 205770_at GSR NM_000637 1935 219684_at RTP4 NM_022147 1936 206448_at ZNF365 NM_014951 1937 222230_s_at ACTR10 NM_018477 1938 1553689_s_at METTL6 NM_152396 1939 226032_at CASP2 NM_032982 1940 242858_at — — 1941 241590_at — — 1942 207113_s_at TNF NM_000594 1943 234886_at TRBV24-1 — 1944 205978_at KL NM_004795 1945 243213_at STAT3 NM_003150 1946 236668_at — — 1947 230683_at ANKRD60 XM_001134442 1948 217747_s_at RPS9 NM_001013 1949 1560228_at SNAI3 NM_178310 1950 233571_x_at PPDPF NM_024299 1951 222214_at — — 1952 220079_s_at USP48 NM_001032730 1953 233401_at — — 1954 223566_s_at BCOR NM_001123383 1955 221964_at TULP3 NM_001160408 1956 212337_at TUG1 NR_002323 1957 201192_s_at PITPNA NM_006224 1958 223217_s_at NFKBIZ NM_001005474 1959 243955_at — — 1960 231810_at BRI3BP NM_080626 1961 218627_at DRAM1 NM_018370 1962 205728_at ODZ1 NM_001163278 1963 238425_at — — 1964 202299_s_at HBXIP NM_006402 1965 203659_s_at TRIM13 NM_001007278 1966 207735_at RNF125 NM_017831 1967 233979_s_at ESPN NM_031475 1968 226300_at MED19 NM_153450 1969 222307_at LOC282997 NR_026932 1970 1559699_at C20orf74 NM_020343 1971 239937_at ZNF207 NM_001032293 1972 211022_s_at ATRX NM_000489 1973 203567_s_at TRIM38 NM_006355 1974 203054_s_at TCTA NM_022171 1975 202905_x_at NBN NM_002485 1976 237870_at LOC285771 — 1977 232044_at RBBP6 NM_006910 1978 201931_at ETFA NM_000126 1979 203694_s_at DHX16 NM_001164239 1980 224652_at CCNY NM_145012 1981 221978_at HLA-F NM_001098478 1982 243995_at PTAR1 NM_001099666 1983 225925_s_at USP48 NM_001032730 1984 204750_s_at DSC2 NM_004949 1985 226606_s_at GTPBP5 NM_015666 1986 232141_at U2AF1 NM_001025203 1987 227535_at C15orf24 NM_020154 1988 1553829_at C2orf58 NR_027252 1989 208853_s_at CANX NM_001024649 1990 212020_s_at MKI67 NM_001145966 1991 225205_at KIF3B NM_004798 1992 232724_at MS4A6A NM_022349 1993 221139_s_at CSAD NM_015989 1994 210495_x_at FN1 NM_002026 1995 210910_s_at POMZP3 NM_012230 1996 1555920_at CBX3 NM_007276 1997 220980_s_at ADPGK NM_031284 1998 1556601_a_at SPATA13 NM_153023 1999 215236_s_at PICALM NM_001008660 2000 240031_at MSRA NM_001135670 2001 234980_at TMEM56 NM_152487 2002 1556865_at — —

TABLE 1A Non- SEQ Log-2 Log ID Fold Fold No. Probe Set ID Change Chng. 1 1560255_at 0.53 144% 2 238712_at 0.29 122% 3 227970_at −0.55 68% 4 237056_at 0.94 192% 5 241981_at 0.86 182% 6 223974_at 0.59 151% 7 241415_at 0.31 124% 8 1565544_at 0.56 147% 9 216693_x_at 0.44 136% 10 1570264_at 0.39 131% 11 221044_s_at 0.19 114% 12 203658_at 0.39 131% 13 213390_at −0.11 93% 14 224832_at −0.37 77% 15 212642_s_at −0.33 80% 16 225005_at −0.22 86% 17 242167_at 0.33 126% 18 221143_at 0.32 125% 19 243824_at 0.62 154% 20 222139_at 0.66 158% 21 237315_at 0.37 129% 22 238360_s_at 0.28 121% 23 239571_at 0.38 130% 24 232356_at 0.24 118% 25 224645_at −0.22 86% 26 1556110_at −0.21 86% 27 234842_at −0.21 86% 28 212017_at −0.24 85% 29 226772_s_at 0.23 117% 30 1552648_a_at −0.32 80% 31 225840_at −0.35 78% 32 202657_s_at −0.27 83% 33 231764_at −0.22 86% 34 232513_x_at 0.42 134% 35 233283_at 0.46 138% 36 206983_at −0.69 62% 37 243654_at 0.61 153% 38 223558_at −0.33 80% 39 225263_at −0.16 90% 40 1563497_at 0.25 119% 41 237655_at 0.65 157% 42 230571_at −0.28 82% 43 238439_at 1.42 268% 44 200666_s_at −0.18 88% 45 235024_at −0.21 86% 46 244025_at 0.15 111% 47 217868_s_at 0.23 117% 48 236229_at 0.43 135% 49 202365_at −0.24 85% 50 208741_at −0.20 87% 51 226585_at −0.30 81% 52 219938_s_at 0.34 127% 53 1554557_at 0.35 127% 54 244492_at 0.50 141% 55 243303_at 0.23 117% 56 216112_at 0.29 122% 57 225494_at −0.32 80% 58 239363_at −0.31 81% 59 1563975_at 0.67 159% 60 242917_at 0.55 146% 61 240661_at 0.37 129% 62 226547_at −0.10 93% 63 231259_s_at −0.51 70% 64 210281_s_at 0.27 121% 65 235543_at 0.33 126% 66 202656_s_at −0.18 88% 67 243399_at 0.44 136% 68 205321_at −0.32 80% 69 230738_at 0.39 131% 70 234302_s_at −0.17 89% 71 231309_at −0.33 80% 72 201751_at −0.22 86% 73 221654_s_at 0.08 106% 74 235058_at −0.26 84% 75 203609_s_at −0.41 75% 76 239845_at 0.31 124% 77 223637_s_at 0.26 120% 78 228156_at 0.23 117% 79 243509_at 0.33 126% 80 238053_at 0.45 137% 81 240733_at 0.31 124% 82 235421_at 0.21 116% 83 204787_at 0.71 164% 84 212806_at 1.21 231% 85 200759_x_at −0.08 95% 86 213174_at −0.48 72% 87 205181_at 0.23 117% 88 242306_at 0.21 116% 89 1562321_at 0.60 152% 90 201578_at 0.10 107% 91 1554132_a_at −0.21 86% 92 225545_at −0.31 81% 93 228811_at −0.47 72% 94 203879_at −0.08 95% 95 201170_s_at −0.43 74% 96 215322_at 0.38 130% 97 1562110_at 0.43 135% 98 226804_at 0.62 154% 99 1554116_s_at 0.45 137% 100 237942_at 0.24 118% 101 219671_at −0.62 65% 102 210104_at −0.16 90% 103 212097_at 0.69 161% 104 1558761_a_at 0.29 122% 105 215888_at 0.24 118% 106 208823_s_at −0.22 86% 107 201731_s_at −0.18 88% 108 220131_at −0.34 79% 109 216233_at 0.60 152% 110 236629_at −0.36 78% 111 1562260_at 0.34 127% 112 33494_at 0.18 113% 113 228068_at −0.17 89% 114 222640_at −0.17 89% 115 239077_at 0.41 133% 116 207040_s_at −0.26 84% 117 228723_at 0.22 116% 118 1562031_at 0.66 158% 119 218771_at −0.13 91% 120 219906_at 0.23 117% 121 217286_s_at −0.17 89% 122 235777_at 0.20 115% 123 218625_at 0.91 188% 124 1567213_at 0.30 123% 125 243276_at −0.43 74% 126 215210_s_at −0.23 85% 127 207339_s_at −0.32 80% 128 202533_s_at 0.12 109% 129 230267_at 0.35 127% 130 218168_s_at −0.30 81% 131 222844_s_at −0.32 80% 132 233264_at 0.33 126% 133 214501_s_at 0.15 111% 134 203505_at 0.54 145% 135 203556_at −0.15 90% 136 226474_at 0.14 110% 137 215640_at 0.29 122% 138 243902_at 0.64 156% 139 208109_s_at 0.15 111% 140 220905_at 0.21 116% 141 201259_s_at −0.20 87% 142 221908_at −0.41 75% 143 205379_at −0.66 63% 144 229374_at −0.39 76% 145 1569666_s_at 0.59 151% 146 228216_at 0.26 120% 147 240217_s_at −0.12 92% 148 218287_s_at −0.20 87% 149 218517_at −0.13 91% 150 244796_at 0.22 116% 151 233976_at 0.21 116% 152 233647_s_at 0.31 124% 153 239489_at 0.27 121% 154 238923_at −0.18 88% 155 237018_at 0.13 109% 156 240176_at 0.40 132% 157 200860_s_at −0.16 90% 158 212639_x_at −0.09 94% 159 201236_s_at −0.16 90% 160 205857_at 0.38 130% 161 228603_at 0.28 121% 162 238728_at −0.25 84% 163 216729_at 0.40 132% 164 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208705_s_at −0.06 96% 1821 203939_at −0.58 67% 1822 217770_at −0.07 95% 1823 233787_at 0.17 113% 1824 241621_at 0.15 111% 1825 1553750_a_at 0.13 109% 1826 211018_at −0.36 78% 1827 200693_at −0.02 99% 1828 218662_s_at 0.26 120% 1829 220990_s_at 0.07 105% 1830 232149_s_at 0.19 114% 1831 232012_at 0.03 102% 1832 1562825_at 0.33 126% 1833 233800_at 0.06 104% 1834 242806_at −0.16 90% 1835 1557544_at 0.41 133% 1836 203923_s_at 0.20 115% 1837 217978_s_at −0.07 95% 1838 220940_at 0.12 109% 1839 221711_s_at −0.12 92% 1840 222087_at −0.35 78% 1841 213201_s_at 0.32 125% 1842 219178_at −0.11 93% 1843 213535_s_at −0.17 89% 1844 204039_at 0.24 118% 1845 215754_at 0.04 103% 1846 232284_at 0.35 127% 1847 204214_s_at 0.23 117% 1848 219765_at −0.32 80% 1849 201938_at 0.09 106% 1850 230009_at 0.16 112% 1851 239427_at −0.38 77% 1852 214717_at −0.12 92% 1853 239755_at 0.02 101% 1854 208322_s_at −0.21 86% 1855 227910_at 0.06 104% 1856 225382_at −0.25 84% 1857 220167_s_at 0.22 116% 1858 1561884_at 0.17 113% 1859 206332_s_at 0.12 109% 1860 220193_at −0.09 94% 1861 237623_at 0.22 116% 1862 209410_s_at 0.53 144% 1863 214481_at 0.23 117% 1864 236511_at 0.19 114% 1865 226184_at 0.35 127% 1866 222955_s_at −0.01 99% 1867 226840_at 0.17 113% 1868 227931_at 0.09 106% 1869 231837_at −0.25 84% 1870 232180_at 0.07 105% 1871 227305_s_at −0.10 93% 1872 237597_at −0.29 82% 1873 1555963_x_at −0.38 77% 1874 211874_s_at −0.19 88% 1875 208541_x_at −0.13 91% 1876 242868_at 0.37 129% 1877 227522_at 0.68 160% 1878 207324_s_at −0.46 73% 1879 1560109_s_at 0.19 114% 1880 231644_at 0.52 143% 1881 1554360_at 0.17 113% 1882 239988_at 0.22 116% 1883 201898_s_at 0.07 105% 1884 1555420_a_at −0.07 95% 1885 209691_s_at 0.01 101% 1886 213596_at 0.27 121% 1887 243767_at 0.13 109% 1888 218014_at −0.17 89% 1889 208179_x_at −0.29 82% 1890 230761_at 0.04 103% 1891 217100_s_at −0.08 95% 1892 242066_at 0.17 113% 1893 232927_at −0.01 99% 1894 217933_s_at 0.26 120% 1895 205929_at −0.38 77% 1896 234049_at −0.16 90% 1897 225435_at −0.05 97% 1898 203560_at 0.37 129% 1899 1555427_s_at 0.04 103% 1900 215127_s_at 0.04 103% 1901 1561211_at −0.17 89% 1902 226509_at 0.17 113% 1903 228088_at 0.17 113% 1904 218126_at −0.02 99% 1905 1559052_s_at −0.11 93% 1906 203311_s_at −0.12 92% 1907 232646_at −0.02 99% 1908 218907_s_at 0.12 109% 1909 232744_x_at 0.13 109% 1910 213310_at −0.05 97% 1911 222900_at −0.12 92% 1912 1552607_at −0.06 96% 1913 232237_at −0.01 99% 1914 1558310_s_at 0.13 109% 1915 233794_at −0.33 80% 1916 207276_at −0.11 93% 1917 228719_at 0.00 100% 1918 200076_s_at 0.01 101% 1919 209750_at −0.30 81% 1920 243033_at 0.11 108% 1921 226210_s_at −0.36 78% 1922 228434_at −0.33 80% 1923 204497_at −0.24 85% 1924 203339_at 0.10 107% 1925 229980_s_at 0.06 104% 1926 213531_s_at −0.09 94% 1927 201487_at 0.11 108% 1928 215169_at −0.16 90% 1929 203342_at 0.06 104% 1930 222879_s_at −0.17 89% 1931 239866_at −0.31 81% 1932 209110_s_at 0.08 106% 1933 214083_at −0.11 93% 1934 205770_at 0.19 114% 1935 219684_at 0.30 123% 1936 206448_at −0.22 86% 1937 222230_s_at 0.10 107% 1938 1553689_s_at 0.12 109% 1939 226032_at −0.04 97% 1940 242858_at 0.08 106% 1941 241590_at 0.22 116% 1942 207113_s_at −0.24 85% 1943 234886_at −0.52 70% 1944 205978_at 0.53 144% 1945 243213_at 0.19 114% 1946 236668_at 0.11 108% 1947 230683_at 0.24 118% 1948 217747_s_at −0.07 95% 1949 1560228_at 0.18 113% 1950 233571_x_at −0.37 77% 1951 222214_at 0.02 101% 1952 220079_s_at 0.08 106% 1953 233401_at −0.20 87% 1954 223566_s_at −0.39 76% 1955 221964_at −0.20 87% 1956 212337_at 0.10 107% 1957 201192_s_at −0.10 93% 1958 223217_s_at 0.20 115% 1959 243955_at 0.50 141% 1960 231810_at −0.17 89% 1961 218627_at 0.28 121% 1962 205728_at 0.68 160% 1963 238425_at −0.23 85% 1964 202299_s_at 0.09 106% 1965 203659_s_at 0.10 107% 1966 207735_at −0.17 89% 1967 233979_s_at −0.40 76% 1968 226300_at −0.10 93% 1969 222307_at −0.17 89% 1970 1559699_at 0.34 127% 1971 239937_at 0.10 107% 1972 211022_s_at 0.00 100% 1973 203567_s_at 0.08 106% 1974 203054_s_at 0.01 101% 1975 202905_x_at 0.21 116% 1976 237870_at 0.45 137% 1977 232044_at 0.07 105% 1978 201931_at 0.12 109% 1979 203694_s_at −0.04 97% 1980 224652_at −0.07 95% 1981 221978_at 0.08 106% 1982 243995_at 0.17 113% 1983 225925_s_at 0.12 109% 1984 204750_s_at 0.34 127% 1985 226606_s_at 0.33 126% 1986 232141_at 0.18 113% 1987 227535_at 0.04 103% 1988 1553829_at 0.30 123% 1989 208853_s_at −0.11 93% 1990 212020_s_at −0.02 99% 1991 225205_at −0.14 91% 1992 232724_at 0.09 106% 1993 221139_s_at 0.21 116% 1994 210495_x_at 0.07 105% 1995 210910_s_at −0.23 85% 1996 1555920_at 0.14 110% 1997 220980_s_at 0.08 106% 1998 1556601_a_at 0.29 122% 1999 215236_s_at 0.02 101% 2000 240031_at 0.24 118% 2001 234980_at 0.53 144% 2002 1556865_at 0.33 126%

TABLE 2 SEQ ID Gene Primary NO Probe Set ID Symbol Transcript ID 2003 201030_x_at LDHB NM_002300 2004 212300_at TXLNA NM_175852 2005 208781_x_at SNX3 NM_003795 2006 234734_s_at TNRC6A NM_014494 2007 213297_at RMND5B NM_022762 2008 226220_at METTL9 NM_001077180 2009 209127_s_at SART3 NM_014706 2010 202022_at ALDOC NM_005165 2011 200066_at IK NM_006083 2012 236065_at — AA782826 2013 217234_s_at EZR NM_001111077 2014 222427_s_at LARS NM_020117 2015 230958_s_at — AA040716 2016 209721_s_at IFFO1 NM_001039670 2017 239673_at — AA618177 2018 215543_s_at LARGE NM_004737 2019 203413_at NELL2 NM_001145107 2020 1556451_at — AA741105 2021 1555355_a_at ETS1 NM_001143820 2022 204178_s_at RBM14 NM_002896 2023 236472_at — AA490889 2024 217911_s_at BAG3 NM_004281 2025 226204_at C22orf29 NM_024627 2026 244549_at LOC100130212 ENST00000406220 2027 223219_s_at CNOT10 NM_015442 2028 204089_x_at MAP3K4 NM_005922 2029 39249_at AQP3 NM_004925 2030 212660_at PHF15 NM_015288 2031 230679_at DCAF10 NM_024345 2032 206453_s_at NDRG2 NM_016250 2033 205081_at CRIP1 NM_001311 2034 201478_s_at DKC1 NM_001142463 2035 221726_at RPL22 NM_000983 2036 211750_x_at TUBA1C NM_032704 2037 223088_x_at ECHDC1 NM_001002030 2038 217884_at NAT10 NM_001144030 2039 214447_at ETS1 NM_001143820 2040 227731_at CNBP NM_001127192 2041 218123_at C21orf59 NM_021254 2042 1558525_at — AK095480 2043 218611_at IER5 NM_016545 2044 211928_at DYNC1H1 NM_001376 2045 212115_at HN1L NM_144570 2046 228585_at ENTPD1 NM_001098175 2047 236172_at LTB4R NM_001143919 2048 229569_at — BC037328 2049 202724_s_at FOXO1 NM_002015 2050 201173_x_at NUDC NM_006600 2051 211997_x_at H3F3B NM_005324 2052 204951_at RHOH NM_004310 2053 1557910_at HSP90AB1 NM_007355 2054 203082_at BMS1 NM_014753 2055 230586_s_at ZNF703 NM_025069 2056 212036_s_at PNN NM_002687 2057 217957_at C16orf80 NM_013242 2058 202683_s_at RNMT NM_003799 2059 237340_at SLC26A8 NM_052961 2060 208758_at ATIC NM_004044 2061 209671_x_at TRA@ M12423 2062 239790_s_at — AI435437 2063 203956_at MORC2 NM_014941 2064 213006_at CEBPD NM_005195 2065 205544_s_at CR2 NM_001006658 2066 218667_at PJA1 NM_001032396 2067 220690_s_at DHRS7B NM_015510 2068 223287_s_at FOXP1 NM_001012505 2069 202521_at CTCF NM_006565 2070 1560797_s_at — BC042086 2071 204947_at E2F1 NM_005225 2072 211623_s_at FBL NM_001436 2073 239196_at ANKRD22 NM_144590 2074 223916_s_at BCOR NM_001123383 2075 228976_at ICOSLG NM_015259 2076 211949_s_at NOLC1 NM_004741 2077 201677_at C3orf37 NM_001006109 2078 201581_at TMX4 NM_021156 2079 243798_at — AI436580 2080 1554413_s_at RUNDC2B NM_001012391 2081 202723_s_at FOXO1 NM_002015 2082 208313_s_at SF1 NM_004630 2083 219737_s_at PCDH9 NM_020403 2084 202481_at DHRS3 NM_004753 2085 32069_at N4BP1 NM_153029 2086 200956_s_at SSRP1 NM_003146 2087 233086_at C20orf106 NM_001012971 2088 212592_at IGJ NM_144646 2089 216863_s_at MORC2 NM_014941 2090 202084_s_at SEC14L1 NM_001039573 2091 235568_at C19orf59 NM_174918 2092 201875_s_at MPZL1 NM_001146191 2093 202577_s_at DDX19A NM_018332 2094 230656_s_at CIRH1A NM_032830 2095 212567_s_at MAP4 NM_001134364 2096 205698_s_at MAP2K6 NM_002758 2097 200997_at RBM4 NM_002896 2098 208857_s_at PCMT1 NM_005389 2099 242714_at — GENSCAN00000051549 2100 231584_s_at — AA977251 2101 201817_at UBE3C NM_014671 2102 206206_at CD180 NM_005582 2103 224301_x_at H2AFJ NM_177925 2104 239292_at — AA243430 2105 200610_s_at NCL NM_005381 2106 219497_s_at BCL11A NM_018014 2107 213545_x_at SNX3 NM_003795 2108 204745_x_at MT1G NM_005950 2109 217157_x_at IGK@ XM_001715827 2110 200792_at XRCC6 NM_001469 2111 227516_at SF3A1 NM_001005409 2112 206472_s_at TLE3 NM_001105192 2113 203634_s_at CPT1A NM_001031847 2114 235334_at ST6GALNAC3 NM_001160011 2115 206057_x_at SPN NM_001030288 2116 200874_s_at NOP56 NM_006392 2117 222895_s_at BCL11B NM_022898 2118 212009_s_at STIP1 NM_006819 2119 210972_x_at TRA@ AK026255 2120 217152_at — AK024136 2121 200632_s_at NDRG1 NM_001135242 2122 208660_at CS NM_004077 2123 238006_at SIN3A NM_001145357 2124 226531_at ORAI1 NM_032790 2125 209188_x_at DR1 NM_001938 2126 209190_s_at DIAPH1 NM_001079812 2127 210574_s_at NUDC NM_006600 2128 210555_s_at NFATC3 NM_004555 2129 201998_at ST6GAL1 NM_003032 2130 229735_s_at — AA283195 2131 200083_at USP22 NM_015276 2132 200921_s_at BTG1 NM_001731 2133 215343_at CCDC88C NM_001080414 2134 211275_s_at GYG1 NM_004130 2135 211643_x_at IGK@ XM_001719095 2136 218143_s_at SCAMP2 NM_005697 2137 211902_x_at TRA@ AY360462 2138 219974_x_at ECHDC1 NM_001002030 2139 225793_at LIX1L NM_153713 2140 210202_s_at BIN1 NM_004305 2141 222435_s_at UBE2J1 NM_016021 2142 203089_s_at HTRA2 NM_013247 2143 201491_at AHSA1 NM_012111 2144 219110_at GAR1 NM_018983 2145 220948_s_at ATP1A1 NM_000701 2146 223993_s_at CNIH4 NM_014184 2147 215524_x_at TRA@ AB305657 2148 222447_at METTL9 NM_001077180 2149 243780_at — AK128410 2150 206461_x_at MT1H NM_005951 2151 200836_s_at MAP4 NM_001134364 2152 1555677_s_at SMC1A NM_006306 2153 200067_x_at SNX3 NM_003795 2154 203685_at BCL2 NM_000633 2155 202354_s_at GTF2F1 NM_002096 2156 212566_at MAP4 NM_001134364 2157 213302_at PFAS NM_012393 2158 217378_x_at LOC100130100 XM_001716310 2159 222481_at FXC1 NM_012192 2160 228639_at — AA251347 2161 237753_at — AA873230 2162 240698_s_at — AA987545 2163 224917_at MIR21 AY699265 2164 243_g_at MAP4 NM_001134364 2165 209604_s_at GATA3 NM_001002295 2166 213957_s_at CEP350 NM_014810 2167 225320_at CCDC109A NM_138357 2168 228926_s_at SMARCA2 NM_003070 2169 217170_at — ENST00000390443 2170 240656_at — AI798924 2171 1554343_a_at STAP1 NM_012108 2172 205530_at ETFDH NM_004453 2173 200825_s_at HYOU1 NM_001130991 2174 217437_s_at TACC1 NM_001122824 2175 208649_s_at VCP NM_007126 2176 236279_at — AA614270 2177 237515_at TMEM56 NM_152487 2178 205126_at VRK2 NM_001130480 2179 212125_at RANGAP1 NM_002883 2180 208577_at HIST1H3C NM_003531 2181 213620_s_at ICAM2 NM_000873 2182 212741_at MAOA NM_000240 2183 212375_at EP400 NM_015409 2184 212109_at HN1L NM_144570 2185 234681_s_at CHD6 NM_032221 2186 218575_at ANAPC1 NM_022662 2187 201176_s_at ARCN1 NM_001142281 2188 222694_at MGC2752 NR_026052 2189 212396_s_at KIAA0090 NM_015047 2190 234964_at TRD@ AK310675 2191 213360_s_at POM121 NM_001099415 2192 214881_s_at UBTF NM_001076683 2193 206337_at CCR7 NM_001838 2194 223304_at SLC37A3 NM_032295 2195 1567214_a_at PNN NM_002687 2196 225135_at SIN3A NM_001145357 2197 204594_s_at SMCR7L NM_019008 2198 200005_at EIF3D NM_003753 2199 226423_at PAQR8 NM_133367 2200 202032_s_at MAN2A2 NM_006122 2201 216905_s_at ST14 NM_021978 2202 225624_at SNX29 NM_001080530 2203 206569_at IL24 NM_006850 2204 218648_at CRTC3 NM_001042574 2205 217497_at TYMP NM_001113755 2206 202518_at BCL7B NM_001707 2207 226987_at RBM15B NM_013286 2208 218091_at AGFG1 NM_001135187 2209 235372_at FCRLA NM_032738 2210 225669_at IFNAR1 NM_000629 2211 204643_s_at ENOX2 NM_006375 2212 212424_at PDCD11 NM_014976 2213 244015_at — AA704163 2214 222126_at AGFG2 NM_006076 2215 210561_s_at WSB1 NM_015626 2216 236796_at — AA129098 2217 243066_at NPL NM_030769 2218 1555261_at — AL832319 2219 204683_at ICAM2 NM_000873 2220 218001_at MRPS2 NM_016034 2221 209850_s_at CDC42EP2 NM_006779 2222 219722_s_at GDPD3 NM_024307 2223 219599_at EIF4B NM_001417 2224 231296_at — ENST00000439273 2225 201614_s_at RUVBL1 NM_003707 2226 243764_at VSIG1 NM_182607 2227 242104_at — AA687144 2228 213646_x_at TUBA1B NM_006082 2229 222696_at AXIN2 NM_004655 2230 242783_at — GENSCAN00000040863 2231 207819_s_at ABCB4 NM_000443 2232 205861_at SPIB NM_003121 2233 224833_at ETS1 NM_001143820 2234 201648_at JAK1 NM_002227 2235 218754_at NOL9 NM_024654 2236 239085_at JDP2 NM_001135047 2237 225898_at WDR54 NM_032118 2238 202953_at C1QB NM_000491 2239 217235_x_at IGL@ AF026926 2240 240515_at — AA779991 2241 227307_at TSPAN18 NM_001031730 2242 215797_at TRAV8-3 X58769 2243 218865_at MOSC1 NM_022746 2244 208723_at USP11 NM_004651 2245 205445_at PRL NM_000948 2246 211300_s_at TP53 NM_000546 2247 233124_s_at ECHDC1 NM_001002030 2248 204642_at S1PR1 NM_001400 2249 219717_at DCAF16 NM_017741 2250 218619_s_at SUV39H1 NM_003173 2251 202906_s_at NBN NM_002485 2252 224311_s_at CAB39 NM_001130849 2253 221969_at — ENST00000358127 2254 229252_at ATG9B NM_173681 2255 1558662_s_at BANK1 NM_001083907 2256 208623_s_at EZR NM_001111077 2257 213087_s_at EEF1D NM_001130053 2258 242606_at — AA360683 2259 216528_at — AL049244 2260 200715_x_at RPL13A NM_012423 2261 217328_at LOC100134017 XM_001717113 2262 227075_at ELP3 NM_018091 2263 219880_at — AK026811 2264 212769_at TLE3 NM_001105192 2265 228309_at — AF090935 2266 205297_s_at CD79B NM_000626 2267 227216_at RLTPR NM_001013838 2268 1562612_at — AK054812 2269 240156_at — AA417099 2270 235096_at LEO1 NM_138792 2271 217480_x_at LOC100287723 XM_001713971 2272 213590_at SLC16A5 NM_004695 2273 206420_at IGSF6 NM_005849 2274 229348_at UBIAD1 NM_013319 2275 206126_at CXCR5 NM_001716 2276 212770_at TLE3 NM_001105192 2277 211161_s_at COL3A1 NM_000090 2278 208663_s_at TTC3 NM_001001894 2279 226055_at ARRDC2 NM_001025604 2280 235965_at — BX648200 2281 200593_s_at HNRNPU NM_004501 2282 212480_at CYTSA NM_001145468 2283 200795_at SPARCL1 NM_001128310 2284 219667_s_at BANK1 NM_001083907 2285 48117_at CCDC101 NM_138414 2286 209702_at FTO NM_001080432 2287 201217_x_at RPL3 NM_000967 2288 208842_s_at GORASP2 NM_015530 2289 1558215_s_at UBTF NM_001076683 2290 212039_x_at RPL3 NM_000967 2291 207008_at IL8RB NM_001557 2292 208687_x_at HSPA8 NM_006597 2293 217099_s_at GEMIN4 NM_015721 2294 229093_at NOS3 NM_000603 2295 200998_s_at CKAP4 NM_006825 2296 224607_s_at SRP68 NM_014230 2297 228181_at SLC30A1 NM_021194 2298 227646_at EBF1 NM_024007 2299 219108_x_at DDX27 NM_001007559 2300 212076_at MLL NM_005933 2301 214179_s_at NFE2L1 NM_003204 2302 201778_s_at KIAA0494 NM_014774 2303 222915_s_at BANK1 NM_001083907 2304 203611_at TERF2 NM_005652 2305 214643_x_at BIN1 NM_004305 2306 215176_x_at LOC100291464 XM_002346408 2307 227336_at DTX1 NM_004416 2308 210616_s_at SEC31A NM_001077206 2309 221575_at SCLY NM_016510 2310 219864_s_at RCAN3 NM_013441 2311 204388_s_at MAOA NM_000240 2312 242309_at — AA424143 2313 209995_s_at TCL1A NM_001098725 2314 223246_s_at STRBP NM_018387 2315 208581_x_at MT1X NM_005952 2316 208664_s_at TTC3 NM_001001894 2317 201115_at POLD2 NM_001127218 2318 219999_at MAN2A2 NM_006122 2319 205254_x_at TCF7 NM_001134851 2320 225006_x_at TH1L NM_198976 2321 205255_x_at TCF7 NM_001134851 2322 225110_at OGFOD1 NM_018233 2323 231697_s_at TMEM49 NM_030938 2324 225245_x_at H2AFJ NM_177925 2325 213351_s_at TMCC1 NM_001017395 2326 201746_at TP53 NM_000546 2327 223565_at MGC29506 NM_016459 2328 209994_s_at ABCB1 NM_000443 2329 217719_at EIF3L NM_016091 2330 203572_s_at TAF6 NM_005641 2331 225352_at SEC62 NM_003262 2332 1552773_at CLEC4D NM_080387 2333 209773_s_at RRM2 NM_001034 2334 209004_s_at FBXL5 NM_012161 2335 214439_x_at BIN1 NM_004305 2336 230175_s_at — ENST00000500586 2337 235154_at TAF3 NM_031923 2338 228496_s_at CRIM1 NM_016441 2339 237071_at — AI342132 2340 227093_at USP36 NM_025090 2341 204484_at PIK3C2B NM_002646 2342 201027_s_at EIF5B NM_015904 2343 225865_x_at TH1L NM_198976 2344 225370_at PYGO2 NM_138300 2345 202230_s_at CHERP NM_006387 2346 219997_s_at COPS7B NM_022730 2347 222920_s_at KIAA0748 NM_001098815 2348 226602_s_at BCR NM_004327 2349 241613_at — AI254302 2350 1563674_at FCRL2 NM_030764 2351 239377_at EIF1AD NM_032325 2352 216212_s_at DKC1 NM_001142463 2353 224281_s_at NGRN NM_001033088 2354 221234_s_at BACH2 NM_021813 2355 204197_s_at RUNX3 NM_001031680 2356 221477_s_at SOD2 NM_000636 2357 200910_at CCT3 NM_001008800 2358 226121_at DHRS13 NM_144683 2359 208858_s_at ESYT1 NM_015292 2360 222686_s_at CPPED1 NM_001099455 2361 209503_s_at PSMC5 NM_002805 2362 201719_s_at EPB41L2 NM_001135554 2363 229382_at C1orf183 NM_019099 2364 212400_at FAM102A NM_001035254 2365 200965_s_at ABLIM1 NM_001003407 2366 207094_at IL8RA NM_000634 2367 227770_at — AA702005 2368 1568852_x_at — BC045735 2369 1563209_a_at MACROD2 NM_001033087 2370 224632_at GPATCH4 NM_015590 2371 232981_s_at SYNRG NM_001163544 2372 203936_s_at MMP9 NM_004994 2373 210156_s_at PCMT1 NM_005389 2374 211313_s_at BAZ1B NM_032408 2375 214626_s_at GANAB NM_198334 2376 233312_at ROPN1L NM_031916 2377 242241_x_at — AW104358 2378 225583_at UXS1 NM_025076 2379 201503_at G3BP1 NM_005754 2380 229513_at STRBP NM_018387 2381 233261_at EBF1 NM_024007 2382 213489_at MAPRE2 NM_001143826 2383 225002_s_at SUMF2 NM_001042469 2384 220384_at TXNDC3 NM_016616 2385 208914_at GGA2 NM_015044 2386 217230_at EZR NM_001111077 2387 200655_s_at CALM1 NM_006888 2388 203500_at GCDH NM_000159 2389 228787_s_at BCAS4 NM_001010974 2390 200671_s_at SPTBN1 NM_003128 2391 224688_at C7orf42 NM_017994 2392 218569_s_at KBTBD4 NM_016506 2393 225296_at ZNF317 NM_020933 2394 216652_s_at DR1 NM_001938 2395 221741_s_at YTHDF1 NM_017798 2396 211645_x_at — ENST00000390254 2397 209092_s_at GLOD4 NM_016080 2398 217925_s_at C6orf106 NM_022758 2399 200702_s_at DDX24 NM_020414 2400 206177_s_at ARG1 NM_000045 2401 216133_at TRD@ ENST00000443611 2402 219122_s_at THG1L NM_017872 2403 213609_s_at SEZ6L NM_021115 2404 217821_s_at WBP11 NM_016312 2405 200619_at SF3B2 NM_006842 2406 218023_s_at FAM53C NM_001135647 2407 38340_at HIP1R NM_003959 2408 203747_at AQP3 NM_004925 2409 213336_at BAZ1B NM_032408 2410 231553_s_at MICAL3 NM_001122731 2411 228325_at KIAA0146 NM_001080394 2412 221790_s_at LDLRAP1 NM_015627 2413 201554_x_at GYG1 NM_004130 2414 216207_x_at IGKC XM_001715827 2415 229934_at — DQ680071 2416 211456_x_at MT1P2 AF333388 2417 204504_s_at HIRIP3 NM_003609 2418 242722_at LMO7 NM_005358 2419 1569932_at NHSL2 NM_001013627 2420 226082_s_at SFRS15 NM_001145444 2421 219073_s_at OSBPL10 NM_017784 2422 226715_at FOXK1 NM_001037165 2423 202474_s_at HCFC1 NM_005334 2424 203980_at FABP4 NM_001442 2425 234370_at VSIG1 NM_182607 2426 232035_at HIST1H4H NM_003543 2427 217938_s_at KCMF1 NM_020122 2428 201270_x_at NUDCD3 NM_015332 2429 210948_s_at LEF1 NM_001130713 2430 200022_at RPL18 NM_000979 2431 208880_s_at PRPF6 NM_012469 2432 208622_s_at EZR NM_001111077 2433 202926_at NBAS NM_015909 2434 226744_at METT10D NM_024086 2435 236465_at RNF175 NM_173662 2436 208875_s_at PAK2 NM_002577 2437 231418_at — AA749348 2438 230865_at LIX1 NM_153234 2439 203051_at BAHD1 NM_014952 2440 217036_at LOC100293679 XM_002345543 2441 230983_at FAM129C NM_001098524 2442 212303_x_at KHSRP NM_003685 2443 210688_s_at CPT1A NM_001031847 2444 1555779_a_at CD79A NM_001783 2445 226069_at PRICKLE1 NM_001144881 2446 210915_x_at TRBC1 AF043180 2447 235281_x_at AHNAK NM_001620 2448 201934_at WDR82 NM_025222 2449 200060_s_at RNPS1 NM_006711 2450 200992_at IPO7 NM_006391 2451 221822_at CCDC101 NM_138414 2452 222169_x_at SH2D3A NM_005490 2453 1552541_at TAGAP NM_054114 2454 208598_s_at HUWE1 NM_031407 2455 220370_s_at USP36 NM_025090 2456 208960_s_at KLF6 NM_001160124 2457 212069_s_at BAT2L NM_013318 2458 220078_at USP48 NM_001032730 2459 201561_s_at CLSTN1 NM_001009566 2460 201611_s_at ICMT NM_012405 2461 226163_at ZBTB9 NM_152735 2462 212257_s_at SMARCA2 NM_003070 2463 213349_at TMCC1 NM_001017395 2464 206980_s_at FLT3LG NM_001459 2465 231662_at ARG1 NM_000045 2466 212802_s_at GAPVD1 NM_015635 2467 210125_s_at BANF1 NM_001143985 2468 211073_x_at RPL3 NM_000967 2469 222450_at PMEPA1 NM_020182 2470 213145_at FBXL14 NM_152441 2471 221744_at DCAF7 NM_005828 2472 227344_at IKZF1 NM_006060 2473 200895_s_at FKBP4 NM_002014 2474 225876_at NIPAL3 NM_020448 2475 1568987_at MGC57346 NR_026680 2476 216981_x_at SPN NM_001030288 2477 1556055_at — U90905 2478 219528_s_at BCL11B NM_022898 2479 201892_s_at IMPDH2 NM_000884 2480 212827_at IGHM BC009851 2481 227420_at TNFAIP8L1 NM_152362 2482 230245_s_at LOC283663 NR_024433 2483 225487_at TMEM18 NM_152834 2484 212617_at ZNF609 NM_015042 2485 218149_s_at ZNF395 NM_018660 2486 218764_at PRKCH NM_006255 2487 215621_s_at IGHD AK057614 2488 212313_at CHMP7 NM_152272 2489 201999_s_at DYNLT1 NM_006519 2490 1560538_at — BC033936 2491 39402_at IL1B NM_000576 2492 216873_s_at ATP8B2 NM_001005855 2493 239827_at C13orf15 NM_014059 2494 205527_s_at GEMIN4 NM_015721 2495 1556737_at LOC388387 NR_027254 2496 212098_at LOC151162 NM_002410 2497 220326_s_at FLJ10357 NM_018071 2498 225875_s_at NIPAL3 NM_020448 2499 224187_x_at HSPA8 NM_006597 2500 226481_at VPRBP NM_014703 2501 212145_at MRPS27 NM_015084 2502 201164_s_at PUM1 NM_001020658 2503 211478_s_at DPP4 NM_001935 2504 220068_at VPREB3 NM_013378 2505 219301_s_at CNTNAP2 NM_014141 2506 243931_at — AA441806 2507 212376_s_at EP400 NM_015409 2508 1556394_a_at — AI912646 2509 242808_at — ENST00000462248 2510 55692_at ELMO2 NM_133171 2511 214359_s_at HSP90AB1 NM_007355 2512 218945_at C16orf68 NM_024109 2513 202117_at ARHGAP1 NM_004308 2514 206464_at BMX NM_001721 2515 202925_s_at PLAGL2 NM_002657 2516 1555751_a_at GEMIN7 NM_001007269 2517 200812_at CCT7 NM_001009570 2518 200694_s_at DDX24 NM_020414 2519 225141_at — CR607695 2520 228065_at BCL9L NM_182557 2521 201853_s_at CDC25B NM_004358 2522 226806_s_at NFIA NM_001134673 2523 214014_at CDC42EP2 NM_006779 2524 208895_s_at DDX18 NM_006773 2525 203648_at TATDN2 NM_014760 2526 212750_at PPP1R16B NM_015568 2527 205419_at GPR183 NM_004951 2528 209251_x_at TUBA1C NM_032704 2529 213772_s_at GGA2 NM_015044 2530 218564_at RFWD3 NM_018124 2531 213079_at TSR2 NM_058163 2532 200957_s_at SSRP1 NM_003146 2533 208621_s_at EZR NM_001111077 2534 209927_s_at C1orf77 NM_015607 2535 202093_s_at PAF1 NM_019088 2536 203580_s_at SLC7A6 NM_001076785 2537 226511_at DCAF10 NM_024345 2538 208445_s_at BAZ1B NM_032408 2539 211938_at EIF4B NM_001417 2540 218031_s_at FOXN3 NM_001085471 2541 41577_at PPP1R16B NM_015568 2542 221090_s_at OGFOD1 NM_018233 2543 201303_at EIF4A3 NM_014740 2544 221123_x_at ZNF395 NM_018660 2545 1556006_s_at CSNK1A1 NM_001025105 2546 200990_at TRIM28 NM_005762 2547 202524_s_at SPOCK2 NM_001134434 2548 229420_at — AK094885 2549 222439_s_at THRAP3 NM_005119 2550 208614_s_at FLNB NM_001164317 2551 221780_s_at DDX27 NM_017895 2552 209603_at GATA3 NM_001002295 2553 221712_s_at WDR74 NM_018093 2554 244313_at CR1 NM_000573 2555 217755_at HN1 NM_001002032 2556 211596_s_at LRIG1 NM_015541 2557 213891_s_at TCF4 NM_001083962 2558 211666_x_at RPL3 NM_000967 2559 211430_s_at IGH@ XM_001718220 2560 222896_at TMEM38A NM_024074 2561 1552772_at CLEC4D NM_080387 2562 202200_s_at SRPK1 NM_003137 2563 201555_at MCM3 NM_002388 2564 219419_at C18orf22 NM_024805 2565 218280_x_at HIST2H2AA3 NM_001040874 2566 244218_at — AI374686 2567 235401_s_at FCRLA NM_032738 2568 1558796_a_at LOC728052 XM_001717850 2569 221549_at GRWD1 NM_031485 2570 221733_s_at GPATCH4 NM_015590 2571 202106_at GOLGA3 NM_005895 2572 231377_at CXorf65 NM_001025265 2573 200916_at TAGLN2 NM_003564 2574 202123_s_at ABL1 NM_005157 2575 225557_at CSRNP1 NM_033027 2576 217767_at C3 NM_000064 2577 205049_s_at CD79A NM_001783 2578 208644_at PARP1 NM_001618 2579 211941_s_at PEBP1 NM_002567 2580 218511_s_at PNPO NM_018129 2581 222352_at — CR621785 2582 202589_at TYMS NM_001071 2583 233126_s_at OLAH NM_001039702 2584 224581_s_at NUCKS1 NM_022731 2585 220730_at ZNF778 NM_182531 2586 221558_s_at LEF1 NM_001130713 2587 1555826_at EPR1 NR_002219 2588 221528_s_at ELMO2 NM_133171 2589 207812_s_at GORASP2 NM_015530 2590 219451_at MSRB2 NM_012228 2591 226116_at — AK022602 2592 238071_at LCN10 NM_001001712 2593 218728_s_at CNIH4 NM_014184 2594 216262_s_at TGIF2 NM_021809 2595 39854_r_at LOC100293124 NM_020376 2596 213564_x_at LDHB NM_002300 2597 227855_at FLJ10357 NM_018071 2598 201076_at NHP2L1 NM_001003796 2599 206031_s_at USP5 NM_001098536 2600 226122_at PLEKHG1 NM_001029884 2601 214615_at P2RY10 NM_014499 2602 239203_at C7orf53 NM_001134468 2603 231830_x_at RAB11FIP1 NM_001002233 2604 229721_x_at DERL3 NM_001002862 2605 1563560_at AHNAK NM_001620 2606 219118_at FKBP11 NM_001143781 2607 230489_at CD5 NM_014207 2608 224732_at CHTF8 NM_001039690 2609 200000_s_at PRPF8 NM_006445 2610 215235_at SPTAN1 NM_001130438 2611 225918_at GLG1 NM_001145666 2612 214130_s_at PDE4DIP NM_001002810 2613 236402_at — AK124257 2614 212360_at AMPD2 NM_004037 2615 200936_at RPL8 NM_000973 2616 225562_at RASA3 NM_007368 2617 206324_s_at DAPK2 NM_014326 2618 201320_at SMARCC2 NM_001130420 2619 201361_at TMEM109 NM_024092 2620 214659_x_at YLPM1 NM_019589 2621 240326_at — AA226458 2622 220507_s_at UPB1 NM_016327 2623 39248_at AQP3 NM_004925 2624 205841_at JAK2 NM_004972 2625 219129_s_at SAP30L NM_001131062 2626 242422_at — AK128316 2627 242109_at SYTL3 NM_001009991 2628 234013_at TRD@ AY232281 2629 212348_s_at KDM1 NM_001009999 2630 211105_s_at NFATC1 NM_006162 2631 226311_at — BC098581 2632 200079_s_at KARS NM_001130089 2633 225796_at PXK NM_017771 2634 208611_s_at SPTAN1 NM_001130438 2635 201090_x_at TUBA1B NM_006082 2636 225628_s_at MLLT6 NM_005937 2637 214635_at CLDN9 NM_020982 2638 228340_at TLE3 NM_001105192 2639 213892_s_at APRT NM_000485 2640 212413_at 38961 NM_015129 2641 201075_s_at SMARCC1 NM_003074 2642 200064_at HSP90AB1 NM_007355 2643 213481_at S100A13 NM_001024210 2644 209558_s_at HIP1R NM_003959 2645 202705_at CCNB2 NM_004701 2646 241388_at — AK097885 2647 211816_x_at FCAR NM_002000 2648 231775_at TNFRSF10A NM_003844 2649 210338_s_at HSPA8 NM_006597 2650 207628_s_at WBSCR22 NM_017528 2651 57082_at LDLRAP1 NM_015627 2652 234574_at — AK024563 2653 200706_s_at LITAF NM_001136472 2654 224837_at FOXP1 NM_001012505 2655 223374_s_at B3GALNT1 NM_001038628 2656 224603_at — BC042949 2657 228659_at — AL832516 2658 218153_at CARS2 NM_024537 2659 211944_at BAT2D1 NM_015172 2660 210809_s_at POSTN NM_001135934 2661 221891_x_at HSPA8 NM_006597 2662 209670_at TRAC BC063385 2663 225130_at ZRANB1 NM_017580 2664 225353_s_at C1QC NM_001114101 2665 202092_s_at ARL2BP NM_012106 2666 219526_at C14orf169 NM_024644 2667 222186_at ZFAND6 NM_019006 2668 212144_at UNC84B NM_015374 2669 212646_at RFTN1 NM_015150 2670 222006_at LETM1 NM_012318 2671 200057_s_at NONO NM_001145408 2672 202578_s_at DDX19A NM_018332 2673 214661_s_at NOP14 NM_003703 2674 201490_s_at PPIF NM_005729 2675 205456_at CD3E NM_000733 2676 202870_s_at CDC20 NM_001255 2677 241329_s_at — GENSCAN00000003786 2678 205238_at TRMT2B NM_024917 2679 207826_s_at ID3 NM_002167 2680 243338_at — GENSCAN00000018368 2681 219892_at TM6SF1 NM_001144903 2682 214129_at PDE4DIP NM_001002810 2683 201080_at PIP4K2B NM_003559 2684 234339_s_at GLTSCR2 NM_015710 2685 221876_at ZNF783 NR_015357 2686 54037_at HPS4 NM_022081 2687 1560433_at — AI476295 2688 227198_at AFF3 NM_001025108 2689 38521_at CD22 NM_001771 2690 200045_at ABCF1 NM_001025091 2691 226981_at MLL NM_005933 2692 1560257_at — AI908420 2693 229064_s_at RCAN3 NM_013441 2694 1558186_s_at CLLU1 NM_001025233 2695 203158_s_at GLS NM_014905 2696 206485_at CD5 NM_014207 2697 221286_s_at MGC29506 NM_016459 2698 202910_s_at CD97 NM_001025160 2699 225852_at ANKRD17 NM_032217 2700 214669_x_at IGKC AB159729 2701 217422_s_at CD22 NM_001771 2702 1558185_at CLLU1 NM_001025233 2703 208152_s_at DDX21 NM_004728 2704 225145_at NCOA5 NM_020967 2705 221011_s_at LBH NM_030915 2706 212641_at HIVEP2 NM_006734 2707 220034_at IRAK3 NM_001142523 2708 204581_at CD22 NM_001771 2709 202845_s_at RALBP1 NM_006788 2710 211058_x_at TUBA1B NM_006082 2711 227173_s_at BACH2 NM_021813 2712 202523_s_at SPOCK2 NM_001134434 2713 226733_at PFKFB2 NM_001018053 2714 1557055_s_at LOC643837 NR_015368 2715 235460_at SNX22 NM_024798 2716 33850_at MAP4 NM_001134364 2717 223350_x_at LIN7C NM_018362 2718 200842_s_at EPRS NM_004446 2719 220476_s_at C1orf183 NM_019099 2720 39318_at TCL1A NM_001098725 2721 213947_s_at NUP210 NM_024923 2722 205173_x_at CD58 NM_001144822 2723 200072_s_at HNRNPM NM_005968 2724 201356_at SF3A1 NM_001005409 2725 204199_at RALGPS1 NM_014636 2726 228898_s_at SMARCB1 NM_001007468 2727 229072_at — AA456828 2728 218251_at MID1IP1 NM_001098790 2729 225134_at SPRYD3 NM_032840 2730 212185_x_at MT2A NM_005953 2731 202692_s_at UBTF NM_001076683 2732 241608_at — AI254302 2733 205926_at IL27RA NM_004843 2734 236437_at — AA779351 2735 209504_s_at PLEKHB1 NM_001130033 2736 221651_x_at IGK@ AJ010442 2737 222428_s_at LARS NM_020117 2738 203547_at CD4 NM_000616 2739 211796_s_at TRBC1 AF043179 2740 232867_at — AI916960 2741 210461_s_at ABLIM1 NM_001003407 2742 214579_at NIPAL3 NM_020448 2743 216457_s_at SF3A1 NM_001005409 2744 227400_at NFIX NM_002501 2745 201183_s_at CHD4 NM_001273 2746 208549_x_at PTMAP7 AF170294 2747 229719_s_at DERL3 NM_001002862 2748 203717_at DPP4 NM_001935 2749 206621_s_at EIF4H NM_022170 2750 232165_at EPPK1 NM_031308 2751 217760_at TRIM44 NM_017583 2752 212139_at GCN1L1 NM_006836

TABLE 3 SEQ ID SEQ ID NO NO from from Table 1 Table 2 Combination Signature 1 43 2125 54 2194 61 2473 103 2480 110 2488 149 2588 206 2635 337 2638 346 457 481 572 796 826 860 903 1007 1154 1175 1245 1426 1732 Combination Signature 2 89 2044 92 2416 149 2458 262 2473 269 2510 565 2538 617 2547 1311 2646 Combination Signature 3 6 2010 12 2021 15 2022 19 2121 36 2139 47 2172 112 2217 118 2282 2301 2323 2340 2345 2349 2496 2509 2511 2538 2547 2550 2616 2642 2711 2712 2732 Combination Signature 4 5 2008 6 2020 8 2031 9 2032 10 2039 11 2040 12 2060 13 2065 14 2072 16 2107 19 2138 21 2146 22 2152 28 2160 29 2162 32 2179 34 2187 36 2189 37 2208 38 2211 43 2213 56 2226 60 2228 71 2234 72 2235 76 2236 77 2243 78 2244 79 2251 80 2253 86 2255 87 2263 89 2277 97 2282 99 2283 100 2291 101 2296 102 2309 105 2310 110 2312 112 2316 113 2318 115 2319 120 2323 129 2326 130 2339 131 2343 132 2348 134 2351 137 2365 141 2374 144 2386 145 2390 149 2408 151 2416 158 2423 164 2430 168 2431 175 2438 178 2441 180 2448 183 2470 187 2480 191 2481 196 2483 199 2484 201 2485 211 2489 244 2494 258 2499 262 2502 271 2503 274 2515 279 2521 285 2523 292 2528 293 2529 302 2538 314 2546 315 2571 331 2578 335 2591 336 2594 341 2603 342 2618 343 2630 346 2645 351 2646 355 2648 356 2650 372 2654 375 2661 377 2663 382 2669 386 2700 398 2711 399 2712 401 2715 406 2719 411 2732 416 2733 421 422 438 439 440 444 453 456 461 462 465 469 493 496 497 505 506 524 526 533 534 543 544 561 566 568 569 572 579 582 599 600 602 604 613 614 617 620 624 631 642 676 677 689 690 702 707 713 718 722 730 732 745 746 760 763 769 771 783 784 785 789 799 800 814 817 824 825 826 828 851 860 865 876 877 878 883 884 890 893 903 907 910 914 923 925 951 960 964 981 987 988 991 996 1000 1007 1019 1021 1027 1071 1078 1096 1108 1111 1118 1127 1149 1160 1162 1164 1176 1197 1222 1226 1228 1255 1292 1293 1296 1311 1321 1327 1344 1350 1367 1372 1381 1393 1398 1407 1485 1509 1535 1552 1553 1566 1610 1634 1637 1664 1667 1681 1717 1732 1740 1771 1810 1877 1898 1926 1932 1946 1961 1962 2001 Combination Signature 5 3 2005 4 2047 8 2068 15 2074 19 2087 22 2101 23 2104 24 2127 30 2134 32 2138 36 2143 37 2147 38 2151 47 2155 51 2162 52 2163 56 2167 60 2190 61 2191 86 2212 87 2215 92 2218 93 2221 99 2226 100 2232 103 2238 107 2242 112 2247 118 2253 130 2266 136 2283 149 2288 154 2290 171 2293 183 2295 188 2301 199 2308 244 2309 258 2310 269 2323 285 2326 297 2334 302 2336 320 2340 331 2342 371 2345 375 2376 378 2385 397 2392 398 2415 399 2420 406 2427 412 2444 417 2453 422 2455 457 2459 461 2464 481 2467 506 2477 515 2489 518 2495 533 2499 561 2502 566 2504 569 2505 572 2513 609 2518 613 2522 617 2523 620 2542 642 2549 690 2550 702 2582 769 2586 785 2588 789 2605 799 2622 810 2634 824 2643 825 2654 826 2655 903 2657 923 2661 925 2665 932 2669 944 2672 997 2687 1010 2688 1038 2697 1043 2699 1096 2700 1118 2711 1175 2714 1296 2716 1407 2721 1535 2724 1611 2732 1681 2735 1732 2742 1946 2747 Combination Signature 6 2 2008 3 2010 5 2013 6 2014 8 2021 12 2022 15 2032 16 2037 19 2039 30 2044 32 2045 34 2046 36 2047 37 2048 43 2049 44 2053 45 2058 47 2060 54 2061 79 2062 83 2068 86 2073 89 2074 92 2075 95 2077 100 2078 103 2081 107 2082 112 2086 118 2087 124 2088 130 2093 136 2095 149 2096 154 2097 157 2098 159 2100 171 2101 172 2104 183 2105 188 2108 196 2110 199 2111 206 2112 256 2116 258 2117 262 2119 269 2121 279 2125 297 2129 302 2130 331 2134 342 2137 356 2138 375 2139 397 2142 398 2143 406 2145 412 2150 417 2151 422 2152 448 2157 461 2160 506 2161 561 2162 566 2163 569 2165 570 2168 617 2169 624 2172 642 2184 696 2186 763 2187 769 2189 799 2191 810 2192 814 2194 824 2195 825 2196 826 2201 932 2202 944 2208 1010 2209 1096 2211 1106 2212 1118 2214 1311 2215 1426 2217 1535 2221 1611 2222 1681 2228 2234 2235 2236 2241 2242 2243 2253 2256 2261 2263 2264 2266 2274 2275 2276 2277 2282 2286 2288 2289 2291 2292 2293 2299 2301 2303 2309 2312 2315 2319 2321 2323 2326 2327 2333 2339 2340 2341 2342 2345 2349 2351 2354 2357 2359 2364 2365 2373 2375 2376 2383 2384 2385 2386 2389 2390 2391 2394 2398 2400 2401 2409 2415 2416 2420 2421 2423 2425 2428 2429 2431 2432 2434 2441 2444 2448 2458 2459 2460 2461 2469 2472 2473 2474 2477 2478 2479 2480 2485 2488 2489 2494 2495 2496 2498 2502 2503 2504 2509 2510 2511 2515 2518 2519 2523 2531 2532 2533 2538 2539 2540 2543 2546 2547 2550 2551 2564 2570 2571 2574 2578 2582 2588 2594 2601 2603 2604 2606 2607 2611 2616 2620 2622 2627 2630 2631 2634 2635 2638 2641 2642 2646 2649 2654 2656 2662 2670 2671 2672 2675 2679 2688 2693 2694 2696 2697 2699 2704 2705 2706 2711 2712 2715 2716 2724 2728 2732 2733 2741 2750 Combination Signature 7 2 2005 3 2007 5 2010 8 2013 9 2014 10 2027 11 2039 13 2044 14 2046 16 2047 17 2048 19 2049 20 2050 21 2051 22 2052 23 2053 24 2055 28 2059 29 2061 30 2062 31 2065 32 2068 34 2073 35 2074 36 2075 37 2080 38 2081 40 2083 41 2084 42 2086 43 2087 45 2089 47 2093 48 2094 49 2095 51 2097 52 2098 54 2100 56 2103 58 2104 60 2106 61 2109 63 2110 69 2111 70 2116 71 2120 72 2127 74 2134 76 2142 77 2145 78 2147 79 2148 80 2150 83 2152 87 2153 89 2154 91 2155 92 2156 93 2157 95 2158 96 2160 97 2161 98 2162 99 2163 100 2164 101 2169 102 2172 103 2179 105 2180 107 2184 108 2190 109 2193 110 2195 111 2200 112 2203 115 2204 117 2206 118 2208 120 2209 121 2212 122 2213 123 2214 124 2215 125 2224 126 2225 127 2227 129 2228 130 2231 132 2232 133 2235 134 2236 136 2241 137 2247 138 2249 139 2253 141 2254 145 2256 146 2270 148 2271 149 2274 153 2276 155 2278 157 2281 158 2283 159 2284 163 2286 164 2288 165 2289 168 2290 171 2293 174 2294 177 2295 178 2296 180 2298 183 2299 185 2300 186 2301 187 2306 188 2308 191 2312 193 2314 196 2319 197 2321 199 2327 201 2328 205 2335 206 2336 207 2337 211 2340 244 2342 255 2345 256 2346 258 2348 259 2349 262 2351 264 2352 269 2354 271 2359 272 2360 274 2367 275 2370 279 2373 281 2376 285 2382 286 2384 289 2385 292 2386 293 2389 297 2390 302 2391 304 2393 310 2398 312 2400 314 2404 315 2413 320 2414 322 2416 325 2420 329 2422 331 2423 333 2430 335 2431 336 2434 337 2436 340 2438 341 2441 342 2444 343 2445 346 2448 350 2450 351 2454 352 2456 355 2457 356 2458 358 2461 363 2463 371 2464 373 2468 375 2472 376 2473 377 2474 378 2475 380 2478 381 2484 382 2485 384 2489 385 2498 386 2500 389 2502 391 2503 392 2504 397 2505 398 2506 399 2509 401 2510 402 2511 404 2515 406 2518 408 2522 411 2523 412 2524 414 2531 416 2533 418 2534 419 2538 420 2539 421 2540 422 2545 423 2547 427 2558 430 2559 436 2562 438 2569 439 2571 440 2574 444 2576 447 2578 448 2582 453 2585 454 2586 456 2587 457 2588 461 2591 462 2601 465 2603 466 2606 469 2607 476 2611 480 2616 481 2618 483 2620 485 2621 486 2622 488 2626 495 2628 496 2630 497 2632 498 2634 503 2638 504 2640 505 2641 507 2642 509 2643 513 2648 514 2649 526 2653 527 2654 530 2656 533 2659 534 2661 537 2663 538 2668 540 2669 544 2671 547 2673 550 2675 552 2676 553 2677 557 2679 561 2683 564 2687 565 2688 566 2695 568 2697 569 2703 570 2705 572 2710 579 2713 583 2705 598 2710 599 2713 603 2719 604 2730 607 2732 609 2741 610 613 614 617 620 624 628 631 636 637 638 642 646 647 653 659 661 662 663 664 666 675 676 677 678 679 680 689 690 692 693 696 707 709 712 713 714 715 718 719 722 725 728 730 732 733 744 745 746 749 750 753 755 756 759 761 763 766 769 770 771 783 784 785 787 789 792 796 799 800 801 804 810 814 817 825 826 829 831 837 844 845 847 851 858 860 865 869 876 877 878 881 884 888 890 892 893 902 903 907 910 916 918 921 925 932 939 940 941 944 951 954 957 960 961 967 971 975 978 981 987 988 991 994 997 999 1000 1002 1007 1010 1013 1014 1018 1019 1021 1034 1038 1043 1064 1066 1068 1070 1071 1072 1076 1078 1080 1093 1096 1106 1108 1117 1118 1121 1122 1123 1127 1136 1137 1140 1141 1146 1154 1160 1162 1164 1166 1167 1168 1170 1171 1172 1175 1178 1179 1181 1183 1184 1187 1189 1197 1199 1214 1215 1218 1219 1222 1226 1228 1230 1231 1234 1237 1239 1242 1245 1247 1255 1258 1266 1268 1271 1285 1288 1290 1292 1293 1294 1295 1296 1297 1303 1305 1311 1313 1322 1327 1334 1344 1350 1351 1367 1368 1370 1372 1376 1381 1382 1383 1387 1389 1390 1393 1394 1397 1398 1400 1407 1408 1426 1429 1431 1433 1436 1438 1441 1445 1460 1466 1472 1481 1482 1509 1517 1518 1525 1529 1530 1534 1535 1536 1543 1544 1549 1550 1552 1553 1564 1566 1567 1568 1571 1572 1583 1584 1586 1587 1602 1605 1610 1611 1612 1619 1624 1626 1627 1629 1634 1637 1640 1641 1644 1663 1664 1666 1681 1682 1685 1690 1692 1701 1717 1719 1724 1731 1732 1740 1747 1756 1757 1767 1771 1774 1778 1789 1809 1817 1821 1848 1862 1866 1876 1877 1878 1884 1893 1895 1898 1906 1921 1923 1925 1932 1946 1947 1948 1954 1959 1961 1962 1969 1976 1981 1983 2001 Combination Signature 8 3 2005 4 2010 6 2017 9 2020 10 2021 11 2022 12 2023 14 2025 16 2026 17 2029 19 2032 22 2036 28 2037 29 2038 30 2040 32 2041 34 2043 41 2045 43 2049 47 2052 51 2060 52 2061 54 2069 56 2070 61 2071 69 2073 70 2075 71 2076 72 2077 74 2078 77 2081 78 2082 79 2083 86 2087 87 2089 89 2093 92 2095 93 2096 95 2100 98 2101 99 2105 100 2108 108 2111 112 2113 113 2116 118 2117 121 2118 124 2119 126 2123 127 2127 130 2129 134 2130 136 2138 137 2139 138 2142 145 2145 146 2147 149 2149 158 2150 159 2151 163 2153 178 2161 183 2162 188 2165 191 2168 206 2169 207 2171 211 2172 256 2183 258 2187 271 2191 285 2195 292 2200 293 2201 297 2202 302 2203 312 2206 314 2207 315 2209 316 2211 336 2212 340 2214 341 2217 349 2218 350 2220 355 2221 356 2222 363 2224 371 2225 372 2226 378 2228 380 2231 382 2234 386 2236 389 2237 406 2239 411 2241 416 2243 417 2246 418 2251 419 2253 427 2256 438 2257 439 2261 440 2268 444 2270 448 2275 462 2276 465 2277 476 2282 485 2287 488 2290 493 2295 496 2296 497 2298 503 2303 506 2306 513 2316 524 2326 526 2335 552 2340 553 2341 561 2342 565 2345 566 2346 568 2351 569 2352 579 2354 599 2356 602 2357 603 2359 604 2364 606 2366 607 2367 613 2369 614 2374 617 2375 622 2376 628 2383 631 2385 637 2386 638 2388 647 2389 659 2390 663 2391 664 2392 679 2395 692 2398 693 2401 702 2404 707 2405 718 2415 719 2416 725 2421 730 2425 755 2427 760 2428 763 2429 766 2433 771 2435 789 2438 799 2441 810 2444 817 2450 824 2454 825 2455 826 2458 828 2459 831 2461 837 2464 851 2466 865 2467 876 2473 877 2475 884 2479 890 2480 892 2481 893 2482 902 2485 907 2486 914 2491 921 2495 944 2499 954 2515 978 2521 988 2524 991 2526 997 2528 999 2531 1002 2537 1005 2538 1019 2539 1034 2541 1038 2542 1071 2545 1078 2546 1093 2547 1111 2549 1117 2550 1118 2551 1127 2556 1136 2558 1154 2567 1160 2569 1162 2570 1164 2573 1167 2574 1175 2585 1183 2586 1187 2588 1211 2598 1219 2601 1242 2603 1255 2604 1258 2605 1261 2606 1268 2607 1285 2610 1288 2616 1292 2621 1295 2622 1297 2624 1311 2628 1344 2631 1368 2632 1372 2633 1381 2635 1389 2643 1390 2646 1407 2647 1417 2654 1426 2655 1429 2659 1436 2661 1440 2662 1460 2665 1485 2666 1517 2668 1529 2669 1533 2670 1536 2671 1544 2674 1549 2676 1552 2679 1553 2683 1566 2688 1572 2692 1584 2694 1586 2695 1605 2696 1610 2697 1611 2698 1619 2701 1626 2703 1637 2705 1664 2706 1681 2711 1724 2713 1731 2715 1732 2716 1771 2719 1789 2722 1810 2724 1877 2726 1884 2727 1895 2732 1898 2733 1921 2735 1925 2738 1926 2741 1946 2748 1952 2751 1954 1976 2001 Combination Signature 9 2 2003 3 2005 4 2006 5 2007 6 2008 8 2009 11 2010 12 2013 14 2014 15 2015 16 2016 17 2017 19 2018 22 2020 23 2021 24 2022 28 2023 29 2025 30 2026 32 2027 34 2029 36 2030 37 2032 38 2034 41 2035 43 2036 44 2037 45 2038 47 2039 48 2040 51 2041 52 2042 54 2043 56 2044 60 2045 61 2046 69 2047 70 2048 72 2049 74 2050 76 2051 77 2052 79 2053 83 2054 86 2055 87 2056 89 2057 92 2058 93 2059 95 2060 98 2061 99 2062 100 2063 101 2065 102 2066 103 2067 107 2068 110 2069 112 2070 118 2071 121 2072 124 2073 126 2074 127 2075 129 2076 130 2077 132 2078 136 2080 137 2081 141 2082 144 2083 145 2084 149 2086 151 2087 154 2088 157 2089 158 2090 159 2093 164 2094 171 2095 172 2096 180 2097 183 2098 188 2099 196 2100 197 2101 199 2103 206 2104 244 2105 255 2106 256 2107 258 2108 262 2109 268 2110 269 2111 271 2112 279 2113 285 2114 292 2115 297 2116 302 2117 320 2118 331 2119 337 2120 342 2121 343 2123 346 2124 351 2125 355 2126 356 2127 360 2128 371 2129 373 2130 375 2131 377 2132 378 2133 385 2134 397 2137 398 2138 399 2139 406 2141 412 2142 416 2143 417 2145 419 2146 421 2147 422 2148 438 2149 439 2150 444 2151 448 2152 453 2153 457 2154 461 2155 462 2156 465 2157 481 2158 485 2160 488 2161 505 2162 506 2163 515 2164 518 2165 524 2166 526 2167 533 2168 544 2169 550 2171 561 2172 565 2173 566 2177 568 2179 569 2180 570 2183 572 2184 579 2186 582 2187 583 2188 599 2189 609 2190 613 2191 617 2192 620 2193 624 2194 631 2195 638 2196 642 2197 646 2198 675 2200 690 2201 696 2202 702 2203 715 2204 730 2205 732 2206 746 2207 755 2208 761 2209 763 2210 769 2211 783 2212 784 2213 785 2214 789 2215 799 2216 810 2217 814 2218 817 2219 824 2220 825 2221 826 2222 831 2223 860 2224 865 2225 877 2226 884 2227 903 2228 907 2230 910 2231 914 2232 923 2234 925 2235 932 2236 944 2237 975 2238 978 2239 991 2240 996 2241 997 2242 999 2243 1000 2244 1010 2245 1038 2246 1043 2247 1068 2248 1072 2249 1096 2251 1106 2252 1117 2253 1118 2254 1136 2255 1154 2256 1164 2257 1175 2258 1184 2261 1197 2263 1219 2264 1234 2266 1242 2268 1285 2270 1292 2271 1293 2272 1296 2274 1303 2275 1311 2276 1350 2277 1390 2278 1398 2279 1407 2280 1426 2281 1436 2282 1453 2283 1517 2284 1525 2285 1529 2286 1535 2287 1544 2288 1552 2289 1566 2290 1611 2291 1612 2292 1627 2293 1634 2294 1667 2295 1681 2296 1731 2297 1732 2298 1740 2299 1789 2300 1810 2301 1884 2303 1895 2304 1898 2306 1926 2307 1946 2308 1947 2309 1952 2310 1976 2312 2001 2314 2315 2316 2317 2318 2319 2321 2323 2324 2325 2326 2327 2328 2329 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2348 2349 2351 2352 2353 2354 2356 2357 2358 2359 2360 2361 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2379 2380 2382 2383 2384 2385 2386 2388 2389 2390 2391 2392 2393 2394 2395 2398 2400 2401 2402 2403 2404 2405 2407 2408 2409 2410 2411 2413 2414 2415 2416 2420 2421 2422 2423 2424 2425 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2438 2439 2440 2441 2442 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2466 2467 2468 2469 2470 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2491 2492 2494 2495 2496 2498 2499 2500 2501 2502 2503 2504 2505 2506 2509 2510 2511 2512 2513 2514 2515 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2528 2529 2531 2532 2533 2534 2535 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2549 2550 2551 2554 2555 2556 2558 2559 2560 2562 2563 2564 2565 2567 2569 2570 2571 2573 2574 2576 2577 2578 2579 2580 2581 2582 2584 2585 2586 2587 2588 2589 2590 2591 2593 2594 2598 2600 2601 2603 2604 2605 2606 2607 2609 2610 2611 2613 2614 2615 2616 2618 2619 2620 2621 2622 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2638 2639 2640 2641 2642 2643 2645 2646 2647 2648 2649 2650 2653 2654 2655 2656 2657 2659 2661 2662 2663 2664 2665 2666 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2679 2681 2682 2683 2686 2687 2688 2689 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2735 2737 2738 2741 2742 2743 2744 2745 2747 2748 2750 2751 2752

TABLE 4 1st Signature 2nd Signature 3rd Signature of 8 (SEQ ID of 8 (SEQ ID of 8 (SEQ ID Nos.) Nos.) Nos.) 3 197 6 36 55 156 112 4 137 34 33 123 2 145 58 1 112 44 12 173 19 35 12 100

TABLE 5 1st Signature 2nd Signature 3rd Signature of 30 (SEQ ID of 30 (SEQ ID of 30 (SEQ ID Nos.) Nos.) Nos.) 112 111 113 2 32 112 3 132 25 1 149 26 12 114 152 34 56 57 35 41 40 36 54 133 5 46 127 7 86 31 115 113 119 37 83 134 14 69 161 118 76 182 121 5 138 8 175 141 15 165 162 10 99 124 127 197 143 39 16 35 17 55 74 9 45 160 18 202 66 124 103 101 38 60 96 4 44 62 40 78 78 19 117 169 20 31 137 130 176 197

TABLE 6 1st Signature of 60 2nd Signature of 60 3rd Signature of 60 (SEQ ID Nos.) (SEQ ID Nos.) (SEQ ID Nos.) 6 104 172 43 65 121 27 30 35 16 53 72 115 173 159 24 189 190 17 16 162 19 92 86 133 82 77 14 7 147 12 22 157 18 193 182 30 164 188 9 33 155 121 160 63 8 188 146 21 48 151 107 98 7 58 192 137 41 43 15 46 13 88 57 156 148 40 41 105 35 133 83 11 57 142 45 70 169 7 117 174 31 180 25 34 185 114 20 158 24 38 83 32 5 62 14 32 165 166 47 195 92 33 149 180 50 11 2 124 126 13 26 59 191 22 116 51 36 184 81 4 94 104 127 27 179 49 42 176 3 128 173 39 71 189 130 190 19 44 31 50 13 114 194 25 36 101 28 3 115 10 5 130 29 196 133 23 140 73 1 49 94 42 159 79 37 191 178 2 86 91 118 139 202 15 171 37 112 138 10

TABLE 7 1st Signature 2nd Signature 3rd Signature of 102 of 102 of 102 (SEQ ID Nos.) (SEQ ID Nos.) (SEQ ID Nos.) 91 2 141 67 110 45 6 181 62 84 113 95 9 174 69 8 13 147 19 83 192 10 91 134 130 168 10 71 103 119 89 185 53 59 128 12 124 49 124 16 41 197 112 28 153 52 26 186 93 167 170 72 202 83 26 120 8 22 124 157 24 191 122 34 121 150 86 19 162 14 11 5 1 84 187 3 99 130 76 104 84 15 27 195 37 151 129 120 6 120 2 161 47 133 187 36 83 12 201 12 17 123 53 42 23 78 170 149 77 111 118 69 117 140 50 108 70 46 48 64 107 112 176 87 142 6 56 173 16 25 47 108 42 189 55 54 43 1 90 132 152 57 53 30 62 55 7 5 127 77 55 39 190 4 87 86 13 123 183 21 166 14 18 3 35 43 81 42 66 94 199 44 56 146 32 165 105 70 62 34 20 50 114 47 97 87 33 89 33 115 21 31 94 34 76 23 145 27 29 131 17 35 61 169 75 182 110 28 51 154 85 5 166 118 177 163 30 152 198 82 159 112 49 63 68 31 158 91 60 60 156 74 25 161 36 190 185 68 149 160 88 86 98 17 178 58 127 93 59 61 141 103 121 85 51 65 1 142 81 195 22 64 129 171 80 101 11 73 76 132 63 70 167 58 52 131 7 126 2 40 138 80 103 95 60 27 66 44 45 196 94 109 200 180 41 40 65 11 144 196 38 24 89 39 44 102

TABLE 8 1st Signature of 202 2nd Signature of 202 3rd Signature of 202 (SEQ ID Nos.) (SEQ ID Nos.) (SEQ ID Nos.) 24 1461 1575 123 1589 1348 21 1995 931 142 1983 1184 17 612 1549 183 1710 1263 47 1024 733 178 1544 786 144 956 1677 109 1740 102 105 1718 892 69 1979 79 67 911 1592 181 1560 1147 172 90 184 79 1454 1116 100 568 1420 191 1365 668 162 1008 828 52 1190 158 35 102 250 51 1854 1073 63 1494 286 72 1947 1563 188 1364 30 133 1105 1727 66 1649 964 97 1480 1912 110 1103 662 68 762 207 150 1741 1123 159 594 238 99 355 367 116 1303 1502 46 889 1086 16 896 183 161 926 579 195 1711 1271 190 905 1095 192 788 210 38 1647 792 169 1210 1860 125 1930 1245 80 1788 80 136 1646 1946 128 1416 1107 141 1793 590 78 1893 1611 146 1107 1468 95 1195 1200 6 1582 44 77 1859 1276 115 1830 242 113 1644 909 174 646 1043 166 1315 793 103 523 1360 19 132 1308 39 184 734 130 724 1579 60 412 840 120 216 112 119 1703 1552 137 1769 1637 148 988 217 32 689 1945 131 117 47 25 893 1664 167 1071 72 85 1036 762 49 42 1079 57 1060 1092 59 1778 1831 197 570 569 186 234 1040 86 1517 738 101 669 84 104 1580 1925 151 1613 826 147 1409 1476 112 1167 784 175 1561 887 189 94 612 43 843 302 4 595 1750 199 810 723 45 986 69 90 711 837 179 1191 1427 158 894 1480 139 288 1032 11 848 288 143 1501 707 184 1907 1701 5 1093 100 62 921 293 82 1336 1400 198 1276 929 56 1302 1817 135 1432 1084 153 645 1569 171 198 1172 140 163 1538 13 1182 1544 10 1267 536 102 1987 1685 48 1201 692 164 1635 1973 187 891 1440 89 492 509 44 1343 1663 138 1656 208 154 1154 1029 91 383 463 107 866 1871 114 811 1928 28 279 1436 170 1142 1671 168 1790 474 108 1664 1753 61 1966 1740 111 1980 1371 145 691 1361 200 1796 1848 64 299 1296 75 1506 500 70 671 1469 73 1942 1805 92 1137 1114 7 1475 308 93 1220 270 177 264 1085 33 267 1459 117 500 306 118 1471 1193 9 1232 101 41 620 960 155 660 126 15 914 345 173 1109 693 98 664 1605 165 1228 923 185 448 459 8 1672 969 18 152 1118 202 69 839 106 217 161 37 62 572 96 190 896 26 865 1418 182 1700 1499 65 944 1311 160 601 476 74 763 1619 149 261 1975 27 268 382 134 1825 1726 2 591 85 30 606 1882 194 868 1832 42 1756 918 201 501 835 196 643 817 71 1588 1337 34 1679 39 124 1607 1626 156 506 1513 53 1988 1560 29 460 1849 126 623 202 129 687 879 84 1720 1411 94 1477 168 36 1563 259 83 126 921 23 1482 956 121 196 832 14 1502 1721 40 1602 199 81 1163 852 152 1225 1988 180 1801 287 176 1305 22 12 546 1757 132 80 561 88 1617 605 20 1578 1852 127 819 812 76 1850 1906 31 1570 27 3 337 362 1 1844 1673 122 433 1806 55 1241 1161 193 1880 518 54 884 106 163 1393 73 22 491 1652 87 1384 1937 58 1006 702 157 654 256 50 857 493

TABLE 9 1st Signature of 2nd Signature of 3rd Signature of 502 (SEQ ID Nos.) 502 (SEQ ID Nos.) 502 (SEQ ID Nos.) 345 129 849 100 1571 894 433 869 1001 209 158 422 242 781 876 443 451 1755 389 1195 1267 86 44 1589 240 1656 1570 201 693 75 214 741 1332 7 454 380 200 1043 93 97 1815 381 133 1871 329 132 1892 35 207 1151 1411 318 615 1866 429 1351 1885 283 1 1266 194 215 853 449 1181 1800 333 35 738 256 628 975 384 252 1367 468 504 708 3 1986 391 404 1028 229 231 713 1382 267 1121 20 153 481 1994 167 241 1058 62 1854 435 325 1613 616 397 1984 1546 154 304 776 4 1977 1379 348 603 759 438 1749 575 253 1504 1163 166 1680 733 295 1016 845 140 1717 756 248 1331 1415 491 1400 1868 53 731 1988 20 193 1437 22 953 148 485 1386 1405 229 244 1849 490 1993 1140 327 854 966 212 1023 87 261 176 1124 273 1930 515 104 1439 950 371 1471 661 108 1649 1195 489 1171 1427 191 466 847 435 70 547 238 1310 1420 83 743 58 10 880 549 90 1404 248 145 9 1689 217 1099 825 24 1559 29 471 777 460 39 1599 1319 315 1832 1272 103 1728 44 249 1615 92 116 1416 1753 399 716 1068 180 269 1560 16 1474 726 356 1926 1389 298 1071 608 246 56 1024 219 361 1773 301 1436 1031 35 1040 1709 96 598 183 310 316 1114 148 483 1603 198 266 1043 199 1681 1938 61 1750 1899 450 178 719 177 601 195 141 1995 993 453 1198 506 364 1402 1798 378 169 1913 363 1647 1084 289 1514 790 408 1486 1529 303 254 641 353 1533 1384 87 1403 111 230 1912 1421 452 441 609 367 1063 826 77 247 1301 475 1228 705 37 1467 251 204 815 873 244 295 206 206 1204 1200 27 736 1750 393 1470 412 178 565 365 275 1223 1809 195 1205 371 317 1787 1653 398 436 1760 461 494 1009 122 174 920 28 905 234 272 1876 765 321 1287 1614 308 433 900 300 1006 764 95 737 1363 375 1112 1152 221 1309 1315 352 756 335 306 589 519 324 813 1392 139 568 1088 277 1270 408 48 1318 766 335 431 1164 284 1585 287 498 1430 54 392 838 934 370 1299 278 445 1371 986 361 513 441 432 872 1021 373 1294 1435 460 1149 123 82 162 60 107 785 152 334 1566 25 169 1037 1371 131 1263 1901 417 943 1335 426 1334 1942 113 1745 1027 188 1747 1906 487 1473 267 264 1097 1010 407 986 1158 446 1714 1083 67 1225 1182 1 888 76 365 1895 1230 17 1464 1667 380 1762 1463 85 323 216 127 1407 105 410 548 621 355 579 2001 109 195 1658 307 1328 313 282 1713 571 423 571 1248 33 1096 439 402 1622 274 280 1224 1306 478 1576 49 386 976 958 305 1343 396 269 1992 1249 176 634 681 420 538 1978 395 249 1453 156 1414 830 112 1943 558 220 550 230 56 775 1943 391 1924 1401 494 1577 1108 174 765 413 29 1268 579 42 1985 207 158 545 798 434 470 1234 481 703 1431 409 824 899 57 355 1949 173 747 192 441 1817 1920 405 96 338 55 768 237 455 460 1636 265 1748 383 259 188 660 309 166 1451 189 350 999 359 1732 1780 342 1799 1220 351 1216 1606 224 1551 461 222 508 1122 290 1822 731 285 1775 1604 211 1545 225 341 1671 1758 123 1573 801 302 1659 944 358 1274 1870 336 1187 1812 390 760 1506 14 1284 560 114 1484 1595 21 957 1692 422 364 1498 241 718 1975 245 203 890 44 1536 388 462 1095 370 486 1307 1542 286 1348 81 225 359 531 138 1910 201 488 715 1783 213 1376 971 437 730 1051 314 1709 1069 480 308 241 376 1540 1123 106 1827 1651 467 951 15 339 991 1731 46 342 1702 192 673 299 98 1581 1534 31 73 43 457 1129 1527 472 820 711 218 366 301 47 418 994 255 955 1831 357 1952 63 5 680 1283 493 1761 1341 331 1882 1 372 1185 1732 150 202 1372 49 859 200 496 275 188 316 1535 1608 183 31 494 444 1236 904 135 111 1399 160 417 737 40 1383 1250 50 925 175 456 529 131 9 732 755 330 15 680 346 704 1333 396 157 990 369 447 121 117 1013 1047 350 864 658 15 870 1221 312 1315 243 165 105 427 157 1010 1622 431 189 576 349 790 592 105 402 815 75 1633 835 184 584 805 412 1873 5 89 121 924 299 1726 1386 152 1690 1768 32 1756 308 329 1143 688 338 992 501 60 1250 1777 18 1293 172 401 1672 1116 276 1883 1867 474 733 1821 76 474 1710 418 1357 1521 115 1816 843 469 984 1243 52 186 813 379 973 179 151 235 141 274 1134 721 2 735 101 463 990 1716 458 1084 1408 208 383 614 11 1190 735 79 1721 349 387 1029 1837 34 1378 914 413 627 1403 501 1262 1744 196 1973 927 465 739 1344 126 641 563 45 327 1412 322 453 1240 252 834 420 332 1468 629 454 670 442 235 518 213 278 309 1192 129 750 389 237 287 421 243 4 1254 436 1354 1235 411 1668 1241 134 1249 350 26 1940 970 415 1265 1535 421 1434 1699 78 893 1825 430 963 1828 92 684 977 210 1499 1416 424 378 1223 144 558 695 203 463 470 497 6 1447 400 766 310 228 619 1584 414 1918 312 382 632 880 130 980 502 257 742 864 388 432 1556 99 1483 342 215 546 1669 223 472 1966 343 421 174 6 230 487 51 438 1628 492 1132 648 500 1478 203 202 238 453 473 1723 247 263 344 1211 470 1421 122 294 744 1543 170 771 511 91 1176 741 250 1135 1205 428 1567 789 185 1044 240 136 525 1349 366 160 488 59 258 1841 464 1017 394 374 873 78 311 1278 277 88 1607 61 163 1260 249 251 829 1687 377 928 479 84 207 178 94 621 831 281 1426 935 439 660 1525 205 1488 281 186 920 536 368 1532 1959 119 1674 86 344 1534 341 360 917 922 394 1578 1457 101 1635 1097 36 810 983 81 1409 79 74 1502 1679 288 1120 1684 254 1425 1685 146 1077 907 381 850 724 459 1572 1933 260 1569 897 23 1242 407 8 1694 1611 466 1719 1273 304 1440 810 38 609 640 451 1711 395 416 949 1940 234 1565 1520 323 1905 374 482 245 1568 120 1087 34 447 1902 1299 13 48 399 232 29 1587 247 457 1854 296 1768 1893 143 857 1865 19 595 1366 495 878 1629 292 1373 1216 442 294 194 190 1625 1072 155 1152 9 383 142 678 271 1948 1918 175 734 33 181 1864 1955 287 580 1322 326 92 1533 162 288 508 64 664 1377 354 1459 739 385 1406 821 236 707 204 425 240 1226 362 996 1485 102 788 1561 128 1391 1153 137 1098 1696 297 1014 1953 25 757 164 72 332 357 111 1012 1413 484 587 38 266 1452 62 110 977 513 403 499 1659 168 216 1675 71 213 153 80 659 212 216 758 1450 121 257 1874 479 623 1713 347 1485 1061 125 1554 1476 268 576 1721 93 1030 1605 164 1598 1736 66 1061 529 291 1784 90 502 1221 1168 270 59 1142 124 1267 1537 293 1627 620 448 1167 354 30 1157 1785 179 1007 811 233 591 156 239 1410 1202 58 701 1580 427 1463 134 63 171 593 171 594 858 69 1442 37 262 389 1610 258 1950 1470 406 1291 1730 159 1907 1432 65 844 1956 328 1720 1930 319 1629 816 187 1501 1704 43 196 696 226 1011 823 118 1285 117 477 573 97 476 1300 431 142 1900 363 193 416 1574 172 1753 967 279 268 1137 197 1715 1862 340 1321 1718 73 133 544 149 840 1576 70 1826 1701 227 1489 67 419 1140 1062 147 1105 753 483 267 484 161 919 1139 182 1158 1186 68 170 541 499 581 1119 337 1757 1513 440 1481 1657 54 1651 1516 320 1050 1511 313 969 670 41 1173 1022 12 1465 140

TABLE 10 1st Signature of 2nd Signature of 3rd Signature of 1002 (SEQ ID Nos.) 1002 (SEQ ID Nos.) 1002 (SEQ ID Nos.) 378 2002 82 339 1375 1617 550 41 1424 211 30 509 186 784 674 163 1342 939 947 639 398 908 1887 797 58 1601 383 918 462 228 600 647 1107 771 1326 1564 853 343 930 203 1217 1266 887 476 954 329 1608 192 171 403 1010 714 1043 914 219 452 679 247 1833 1993 415 1278 304 510 882 1740 204 546 199 274 1259 1514 982 1322 1732 519 193 250 38 547 388 270 315 659 948 1028 980 566 1545 119 703 1984 1226 814 1873 728 953 302 597 761 43 140 745 1407 214 645 448 1726 7 1401 1287 588 623 973 257 1533 1244 39 477 495 223 525 26 264 1042 533 488 465 725 828 799 496 333 1785 1647 161 112 73 433 1332 1438 940 383 1022 877 361 202 622 1265 295 79 1522 729 585 612 44 552 938 1949 616 1793 378 103 1479 205 556 310 1528 107 952 361 632 604 1207 612 113 1959 436 348 1479 420 796 1540 822 1712 443 81 1460 144 250 1697 1508 825 1359 365 866 1731 896 648 130 159 966 1100 127 928 526 489 289 889 673 480 1655 1532 965 445 952 266 279 427 59 1578 63 330 63 1346 610 1353 113 541 1818 1283 624 1884 1952 462 1708 1261 647 1617 552 192 1749 384 555 829 459 188 1634 475 335 419 215 863 758 265 810 761 1136 513 577 1385 33 865 347 180 1879 1296 868 478 297 338 1456 65 823 1863 539 629 1365 847 438 1053 1642 244 153 262 395 1905 1182 345 413 601 873 675 1036 945 1685 93 150 1001 1580 368 1004 1432 123 493 323 830 125 1811 97 225 1004 468 1468 1449 46 767 1410 669 181 1309 455 934 542 261 705 1888 845 1800 706 296 1510 1699 544 110 610 657 507 1838 370 1754 1240 551 1002 704 696 883 1995 73 551 35 278 731 876 786 1056 1389 442 1849 1021 871 644 1361 819 1895 1442 472 1742 1844 487 1603 1566 854 470 1868 509 1536 1378 396 1616 870 423 184 30 48 1255 1444 664 808 1945 342 1026 966 662 1083 592 405 1039 1612 376 514 642 141 1819 1364 962 1323 909 800 1339 599 72 1556 434 746 1038 430 633 1070 1308 119 1193 169 730 549 875 685 1425 1900 707 762 1562 741 1133 737 198 839 455 282 1779 1704 527 242 25 197 933 1333 324 169 1546 605 492 694 2 176 469 650 1821 473 355 314 282 546 1307 1159 792 1674 71 677 1837 1277 672 1804 1891 540 685 1151 914 654 94 144 115 1737 958 1184 2000 725 1335 1889 813 6 668 308 1838 1724 327 509 1461 85 524 1954 636 1719 1000 213 846 822 978 561 128 973 1077 946 437 1775 1426 721 900 1023 659 1182 1836 222 71 1050 388 357 1675 949 436 1108 938 1660 1711 996 473 1967 466 1899 1295 915 481 224 142 1347 1753 712 1677 1904 248 719 796 90 562 1545 773 101 1695 842 989 1123 826 1201 471 31 725 1429 852 1331 1314 236 1600 415 54 906 252 434 32 734 312 33 1409 502 1563 1591 705 1748 1618 444 804 1106 379 1978 526 910 82 1550 848 1453 185 815 1264 1325 286 782 1423 493 1412 1256 11 1728 148 86 620 781 748 1572 1115 968 88 588 467 1564 1826 331 59 104 8 187 66 478 866 1855 747 389 1881 242 1862 274 359 1198 853 975 1764 181 254 1137 330 581 572 766 506 489 564 620 338 1656 389 394 672 542 1321 338 857 1834 332 209 369 219 162 888 1843 627 975 29 271 135 1221 439 432 135 559 406 1529 383 516 755 484 408 1217 639 177 888 386 1841 1791 457 1118 994 775 479 431 512 266 9 424 1174 1357 682 1612 1759 481 908 153 593 979 657 572 221 1222 276 427 774 569 827 273 533 580 152 537 1428 540 681 1009 1963 416 617 211 30 677 1513 373 1752 180 146 605 512 686 285 1771 158 1462 1682 298 996 820 187 1810 1387 108 443 603 387 505 568 153 375 412 323 480 1985 445 208 934 221 1638 351 350 1717 166 652 1688 806 256 949 1103 553 885 1501 84 1500 710 409 1440 787 326 809 1899 428 700 1701 432 183 422 195 1668 348 392 718 1908 788 44 1827 766 1308 111 950 487 1976 638 847 1609 937 1442 1797 523 822 1077 503 1766 488 955 23 1088 829 1015 257 253 1357 80 101 1876 1431 179 633 1181 479 318 836 380 843 1883 285 971 1567 357 211 1661 906 26 238 34 126 821 404 895 598 959 1117 43 365 674 1121 983 1974 1065 1000 1311 1817 538 1360 1586 957 146 1493 584 1938 1042 147 334 14 943 1720 1766 781 751 1030 642 942 1158 611 1210 1630 140 779 1597 401 610 450 314 1073 1864 215 93 1196 414 155 849 903 648 1898 1002 2001 284 183 1770 1565 220 69 600 260 1723 1748 283 1145 582 839 56 895 601 1826 184 762 1558 1197 674 1078 357 528 1998 967 106 145 924 970 1109 778 525 1084 108 43 1475 380 912 1140 1798 532 51 1977 507 229 1821 104 151 1750 226 1598 78 644 995 1171 635 504 1053 517 1253 1587 473 1692 130 739 1822 1953 37 1370 230 322 485 775 425 928 837 45 1406 1589 782 1904 627 441 567 360 609 1305 735 374 531 1727 811 1292 593 656 458 548 64 925 1155 514 1069 1789 245 964 523 157 1029 1329 184 1619 943 485 877 1365 995 1788 917 127 1351 840 589 1715 919 384 867 1214 110 1553 1645 840 1711 501 360 853 1312 411 1294 461 131 687 1380 349 395 397 673 738 1480 343 307 1194 435 919 1575 225 1580 283 497 1932 556 214 849 118 302 1444 1707 920 686 362 35 1654 1876 695 95 354 922 1567 1497 753 446 970 288 823 1506 352 1023 968 456 1981 1286 165 1413 1105 684 824 149 595 811 921 284 4 887 892 1672 372 543 892 385 851 133 544 750 352 577 665 1963 1001 838 366 1542 25 340 364 932 1544 1806 615 154 1533 990 1312 1668 902 74 841 891 36 1269 83 1761 1006 26 598 1421 68 1429 1684 277 1075 1319 663 1700 1895 895 1149 1762 582 1492 1637 878 1869 590 535 891 931 363 816 1794 688 161 962 109 1573 1646 407 186 188 856 1767 1447 625 1455 1204 320 1797 1381 641 544 253 849 1156 50 132 92 1414 843 670 1780 102 643 1078 844 878 1445 88 1093 1215 216 192 1937 631 1796 1160 716 1318 254 351 182 1091 313 1855 862 492 864 416 860 1363 1735 876 1239 1991 643 1049 975 755 1618 665 24 247 339 461 1493 563 16 410 1628 262 1703 1683 397 679 212 628 1473 1495 770 274 1773 752 1716 1970 754 1325 56 464 1514 173 744 599 504 654 1727 686 778 1403 1161 476 474 343 328 626 977 297 96 1847 924 1584 684 280 233 1957 794 1330 1700 233 1079 763 450 78 633 19 1705 519 23 978 77 52 1557 1834 690 1642 1344 418 1590 770 816 1664 1120 592 961 1925 55 1431 1734 177 1391 1321 118 1611 920 881 573 730 367 137 1126 759 141 186 421 1020 1964 56 1983 312 175 616 411 577 1096 1459 89 1159 1535 756 1035 1033 427 24 1894 255 1643 306 71 921 1097 679 203 932 237 1787 39 268 1374 792 602 807 1677 751 1112 897 124 1565 998 402 106 1304 874 1495 1290 191 415 750 864 764 55 927 1867 1989 976 1945 1561 22 328 1818 818 1213 151 738 1358 1188 909 1204 1691 208 548 1930 606 665 197 666 1289 1590 178 600 1345 740 1181 691 964 377 349 795 1154 417 486 306 1621 570 459 583 419 559 337 495 1798 1218 767 1701 331 832 1076 1190 733 118 387 92 1300 97 159 1615 923 522 820 1427 769 1953 243 453 1235 1688 13 1098 648 496 1777 2001 571 1269 941 372 954 198 963 215 171 10 1756 99 926 1287 162 689 721 877 524 1346 1702 263 1085 833 630 1212 1840 426 773 1148 837 1732 1572 896 1236 17 114 1334 795 859 1463 900 870 5 893 78 1092 1220 554 1933 937 780 251 779 583 270 720 167 156 121 307 1054 630 465 355 370 574 1592 448 143 205 957 875 331 1184 545 1144 1819 692 1530 345 697 35 1135 318 180 1260 490 1559 356 403 873 865 676 319 457 719 1627 145 98 1288 1553 494 1686 1094 111 1928 90 133 1101 1906 336 1362 377 449 1186 1512 172 196 292 905 1438 819 568 1390 1722 824 345 1363 489 75 240 128 1273 1377 972 1266 1264 454 1482 477 898 1653 1454 687 19 643 82 760 435 921 1405 1237 889 257 447 722 1291 261 66 652 1203 539 262 1615 980 308 878 699 1461 904 139 936 136 475 999 329 358 227 4 660 1010 210 702 658 550 246 774 768 218 631 817 196 1940 803 12 1244 259 981 449 1582 894 1416 45 846 579 106 835 1045 222 224 1645 1850 51 1 1792 821 198 1398 901 1280 1690 586 1639 498 637 606 41 18 330 244 463 940 1912 708 267 1056 505 845 1472 960 1483 52 749 1589 799 122 798 1951 704 556 1104 240 1424 251 999 1551 1086 382 42 788 340 1835 546 997 1805 813 251 1454 114 371 281 608 604 1803 1401 346 625 401 717 163 874 723 1169 1721 808 1024 1327 154 2000 1054 1001 519 22 44 472 974 235 109 1162 483 418 81 477 948 964 74 232 1652 706 127 1960 931 1606 1289 377 1143 382 619 1408 1163 890 431 358 47 1650 732 390 1540 191 408 392 1779 783 372 293 907 213 1706 833 10 1183 804 399 873 597 1484 1781 265 1539 1510 776 255 662 135 1371 1416 176 55 1955 14 397 142 229 280 560 1 899 628 292 67 58 168 122 1602 120 1625 851 393 1714 688 831 1976 767 936 1271 1328 667 438 989 279 70 1853 430 164 1300 925 1386 1040 182 1214 1672 500 694 650 806 494 669 834 793 395 100 536 1915 190 296 579 93 868 1574 591 634 472 594 962 441 711 1823 1509 194 422 1386 944 1885 1743 790 966 1436 76 1679 16 520 1635 1692 364 1597 1940 287 1917 882 515 1814 445 21 373 233 698 874 48 429 1902 1974 969 859 1015 715 1562 1803 649 1626 740 136 1646 1969 580 1445 1141 239 1734 547 796 998 1623 989 1857 1246 530 913 641 319 1669 1638 777 321 1388 529 659 1330 807 965 736 791 1064 988 879 1665 1437 855 1314 848 809 358 1085 151 792 1578 42 1195 1659 575 681 107 399 523 1907 417 1336 1249 53 1911 1712 249 768 772 930 1943 1441 614 724 275 210 1107 1962 919 1369 1708 841 977 545 69 1878 223 17 691 217 886 1228 64 446 490 1592 148 237 1697 578 1662 237 858 277 1917 230 1276 363 531 27 1243 946 1231 1913 95 1209 645 670 1258 828 977 1929 521 356 755 1901 728 581 986 105 1903 814 954 1759 3 413 1729 993 867 1842 137 334 1699 1292 713 1535 1980 301 1304 1786 498 1250 517 217 1447 1556 917 49 1405 939 150 1336 3 1531 1531 504 835 867 36 1080 494 979 941 1746 252 680 206 683 914 1117 303 1636 886 734 1760 201 563 25 922 394 1735 101 757 316 1869 988 1443 1504 138 1260 999 735 116 693 998 1115 1880 511 1931 1076 603 1261 374 884 272 1039 897 1298 303 269 76 791 850 944 1291 347 584 451 294 1893 638 732 66 1213 353 655 623 801 365 32 694 716 2002 561 236 110 344 1656 1349 785 805 318 827 983 190 470 1052 1408 126 709 1599 789 1518 555 817 1815 1415 587 766 1172 700 645 1340 916 576 717 155 108 953 929 439 1608 608 324 1059 758 283 574 316 1194 178 802 1400 1149 201 870 1068 764 990 195 820 83 1926 548 723 1559 32 1913 1383 518 1613 1031 640 1364 676 193 1366 474 579 683 890 451 1449 1017 293 1772 1832 112 704 655 99 428 1896 499 1068 187 134 907 1371 207 1060 1044 935 1397 1549 607 1238 748 560 400 311 166 1262 1648 306 1790 1348 57 1132 752 793 344 1083 152 294 277 258 794 690 325 1915 1833 760 894 1425 398 195 745 228 1094 913 121 1421 479 803 698 193 295 837 530 880 371 290 675 1163 1696 275 1383 1933 281 911 1034 991 263 682 763 1820 410 862 1632 1804 385 701 661 206 1356 466 164 1352 1297 406 1379 916 985 828 1469 243 1504 1307 952 672 5 309 1399 1938 913 1824 436 49 1501 1641 227 360 929 774 678 1404 202 1508 960 77 393 313 634 1961 525 934 1607 656 348 223 1187 80 148 1299 742 412 670 304 1973 1046 799 311 567 20 649 267 724 555 1175 199 1457 1400 993 789 1211 332 912 990 117 197 1544 613 1290 85 491 1667 1475 883 1955 708 564 1124 305 391 1997 955 65 1162 62 967 776 884 354 1343 536 536 1178 715 75 628 951 599 729 1576 156 386 707 9 841 1027 234 1817 722 200 924 420 87 1982 1718 5 202 42 772 290 589 459 1247 425 576 1197 635 731 917 1124 516 1458 1272 169 1836 1118 315 857 158 29 930 1390 658 102 1653 671 863 125 521 64 636 267 1591 157 272 728 1555 986 1909 34 170 1757 1793 779 557 1548 951 671 437 482 1476 992 412 506 74 798 234 947 736 713 1025 590 1417 1063 130 664 683 933 1801 248 646 97 801 259 1710 1823 743 214 299 626 1898 625 547 956 1787 474 1949 1657 62 1361 46 941 968 1687 956 1534 557 129 1114 1624 381 1393 333 91 1736 1941 737 558 1505 526 185 264 361 354 67 189 970 68 174 1512 1471 534 144 1845 447 1395 1110 847 1337 541 992 947 1956 366 541 639 471 1224 1741 565 1581 965 28 460 155 508 173 1606 400 1341 502 160 1576 1391 61 1205 2 311 1532 1496 310 1340 1282 718 754 245 173 1410 1443 787 168 516 893 1372 1164 617 1062 1878 861 1965 1914 375 991 7 655 553 1783 27 568 1463 469 1122 1262 341 1799 1052 232 1241 805 362 592 270 212 1789 1568 729 200 288 812 1277 1830 653 903 756 621 134 285 710 651 538 942 336 702 720 1771 1043 115 1478 714 185 1283 392 836 1167 369 458 1865 961 726 123 1519 904 871 126 709 533 1411 994 512 1092 701 364 89 205 1741 727 885 1552 460 460 1924 1547 784 529 808 797 862 835 63 1427 247 678 1252 1874 116 332 1165 618 630 1720 661 511 402 96 1433 903 273 409 1073 6 1594 566 181 539 1934 410 1313 595 60 708 129 727 387 1338 431 1470 1490 971 893 47 125 1420 1728 67 500 1865 562 396 956 443 880 1795 558 1377 1523 321 745 972 448 1774 1285 440 453 602 668 1900 269 300 1631 1057 41 248 1127 984 207 409 567 1066 174 596 876 1235 557 1975 894 768 303 1066 452 1695 1600 765 1950 1224 113 1737 762 691 1586 98 987 1516 1273 70 1769 289 290 1747 95 4 149 1116 291 1624 534 623 1380 1910 50 775 751 137 1067 1943 145 346 309 869 131 1352 865 353 325 923 1119 1585 693 1554 1229 549 950 394 974 1892 987 317 1676 418 15 727 1767 961 769 319 305 254 1893 573 1183 1199 598 1604 423 680 714 6 872 1121 272 149 955 843 422 1219 386 888 1471 1534 337 1110 1928 40 1086 1579 231 1467 1457 94 1249 1248 241 503 1997 911 1082 1339 299 1847 396 882 235 500 501 1780 891 900 1509 543 651 378 1450 238 1889 572 899 268 861 369 1071 1016 805 103 505

TABLE 11 1^(st) Example for 2^(nd) Example for 3^(rd) Example for Exchange of 50 Exchange of 50 Exchange of 50 within 202 within 202 within 202 Dropped Added Dropped Added Dropped Added 64 451 43 1253 131 847 155 322 113 515 167 1396 162 483 151 815 53 390 72 449 160 508 55 1538 127 388 185 1256 179 1530 103 268 198 898 145 595 102 465 111 1943 82 451 63 236 67 1030 119 891 118 243 98 777 121 844 68 536 106 229 133 1348 44 426 164 927 60 1745 161 395 133 602 134 1631 38 479 8 1643 180 975 157 450 85 1910 42 585 36 218 5 1790 160 1571 120 260 145 1019 6 1215 109 226 93 1888 102 406 37 491 174 1580 72 642 184 529 131 1220 104 596 154 252 41 441 158 1878 169 308 152 1870 185 1456 96 482 20 1467 77 1453 115 458 197 1254 116 1414 144 473 42 314 20 1683 2 434 94 1686 5 771 188 312 171 1610 63 1306 65 486 116 216 1 217 124 315 189 1430 128 470 39 367 161 1514 152 1209 94 270 19 423 124 1253 107 242 6 305 39 1990 34 454 137 1017 79 1950 47 344 122 1101 135 1230 187 323 135 323 30 837 173 351 156 470 75 715 62 433 167 2002 61 709 166 221 53 753 149 1068 136 513 16 1029 202 952 133 338 100 1283 138 1603 45 505 65 479 7 1602 158 385 78 1491 110 582 121 494 143 1617 36 1499 31 386 186 1111 96 1285 130 225 199 1378 197 506 106 481 74 1909 9 1424 95 348 29 298 188 301 112 431 108 959 190 923 137 309 107 1688 166 657 43 461 2 347 144 1604 172 262 101 469 43 1852

TABLE 12 1^(st) Example for 2^(nd) Example for 3^(rd) Example for Exchange of 50 Exchange of 50 Exchange of 50 within 1002 within 1002 within 1002 Dropped Added Dropped Added Dropped Added 106 1070 33 1219 9 1711 45 1030 130 1932 16 1042 65 1050 50 1142 46 1722 124 1018 26 1528 121 1211 118 1155 3 1160 45 1985 169 1202 6 1847 7 1424 161 1049 17 1990 8 1007 144 1099 8 1477 4 1710 107 1158 25 1569 34 1398 187 1093 34 1044 35 1725 2 1119 15 1768 10 1585 130 1143 13 1052 43 1771 172 1036 9 1401 5 1341 68 1033 121 1962 42 1153 96 1172 16 1591 47 1513 39 1005 27 1525 18 1714 136 1241 2 1223 22 1178 115 1220 118 1734 3 1124 102 1246 1 1147 28 1807 133 1032 124 1577 14 1782 62 1069 45 1358 20 1571 127 1170 44 1531 19 1971 38 1079 5 1445 27 1975 166 1132 46 1772 25 1802 36 1268 35 1096 38 1762 63 1097 23 1200 57 1273 109 1179 11 1034 107 1363 94 1197 115 1251 115 1039 120 1236 58 1396 31 1769 173 1142 120 1210 23 1253 64 1173 39 1536 58 1943 31 1058 22 1767 39 1848 112 1183 107 1910 2 1564 137 1008 29 1352 40 1844 157 1112 42 1789 44 1898 154 1178 10 1928 112 1361 37 1121 14 1325 124 1800 47 1270 49 1045 29 1135 184 1180 133 1833 37 1438 95 1249 20 1690 127 1501 103 1126 28 1250 32 1578 155 1224 37 1452 11 1750 188 1251 112 1460 33 1173 158 1113 38 1166 15 1839 72 1125 31 1312 17 1631 34 1269 19 1844 36 1008 162 1062 21 1821 12 1930 44 1203 43 1041 26 1291 43 1004 36 1507 118 1082 121 1148 32 1533 130 1526

TABLE 13 SEQ ID No. 2 7 10 17 19 21 22 23 24 26 27 35 37 41 46 48 54 55 56 58 63 65 67 74 76 78 79 81 88 93 96 97 100 111 117 129 132 138 140 145 146 147 150 151 153 155 156 161 162 163 167 170 180 181 182 184 189 193 198 218 221 225 226 236 242 243 252 260 262 268 270 308 309 312 315 318 322 323 333 338 344 348 351 367 385 386 388 395 410 413 414 426 431 433 434 442 449 450 451 454 458 461 465 473 479 480 481 482 483 486 491 494 505 513 529 536 537 540 548 552 558 562 569 575 577 578 587 597 598 600 601 602 612 614 617 619 623 626 627 628 632 636 643 645 646 653 654 655 656 657 665 668 670 677 688 690 699 701 706 714 718 723 731 733 734 735 739 744 751 753 756 759 769 771 773 778 782 783 799 806 816 819 820 827 839 844 845 847 850 851 858 860 862 863 873 879 890 891 895 908 915 919 929 930 944 946 947 954 961 967 968 983 1001 1004 1005 1008 1010 1018 1030 1032 1033 1036 1049 1050 1055 1058 1062 1069 1070 1079 1092 1093 1097 1099 1112 1113 1119 1121 1125 1126 1132 1133 1134 1142 1143 1148 1155 1158 1170 1172 1173 1178 1179 1180 1183 1184 1186 1187 1197 1202 1203 1220 1224 1236 1241 1246 1249 1251 1268 1269 1270 1271 1275 1284 1297 1301 1303 1306 1314 1319 1320 1325 1329 1330 1331 1333 1335 1337 1349 1352 1357 1358 1359 1362 1364 1366 1367 1377 1380 1395 1403 1409 1415 1425 1427 1428 1446 1449 1452 1460 1466 1471 1477 1479 1483 1485 1488 1491 1492 1493 1512 1521 1523 1524 1527 1528 1539 1540 1547 1550 1557 1567 1568 1569 1574 1590 1602 1613 1617 1618 1620 1629 1631 1632 1635 1645 1650 1654 1655 1658 1661 1674 1680 1685 1687 1688 1692 1695 1711 1713 1721 1725 1740 1742 1744 1750 1755 1761 1762 1765 1766 1767 1780 1783 1788 1791 1803 1805 1809 1810 1814 1832 1833 1834 1851 1853 1864 1872 1876 1879 1880 1882 1887 1890 1892 1893 1901 1909 1915 1931 1940 1941 1946 1951 1953 1959 1963 2002 

1. A method for the detection of colorectal cancer in a human subject based on RNA from a blood sample obtained from said subject, comprising: measuring the abundance of at least 8 RNAs in the sample, that are chosen from the RNAs listed in table 1, and concluding based on the measured abundance whether the subject has colorectal cancer.
 2. The method of claim 1, wherein the abundance of at least 30 RNAs, of at least 60 RNAs, of at least 102 RNAs, of at least 202 RNAs, of at least 502 RNAs, of at least 1002 RNAs, or of at least 2002 RNAs that are chosen from the RNAs listed in table 1 is measured.
 3. The method of claim 1, wherein the abundance of a set of 8 RNAs as listed in table 4 is measured.
 4. The method of claim 2, wherein the abundance of a set of 30 RNAs as listed in table 5 is measured.
 5. The method of claim 2, wherein the abundance of a set of 60 RNAs as listed in table 6 is measured.
 6. The method of claim 42, wherein the abundance of a set of 102 RNAs as listed in table 7 is measured.
 7. The method of claim 2, wherein the abundance of the 202 RNAs of table 8 is measured.
 8. The method of claim 2, wherein the abundance of at least the 502 RNAs of table 9 is measured.
 9. The method of claim 2, wherein the abundance of at least the 1002 RNAs of table 10 is measured.
 10. The method of claim 2, wherein the abundance of at least the 2002 RNAs of table 1 is measured.
 11. The method of claims 1, 2 and 7, wherein the abundance of at least 202 RNAs is measured, wherein at least 152 of the 202 measured RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 1 to 202, and up to 50 of the remaining measured RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 203 to
 2002. 12. The method of claims 1, 2, and 9, wherein the abundance of at least 1002 RNAs is measured, wherein at least 952 of the 1002 measured RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 1 to 1002, and up to 50 of the remaining RNAs are chosen from the group of RNAs that are listed in table 1 and are referred to therein as SEQ ID NOs. 1003 to
 2002. 13. The method of claims 1 to 12, wherein the measuring of RNA abundance is performed using a microarray, a real-time polymerase chain reaction or sequencing.
 14. The method of claims 1 to 13, wherein the decision whether the subject has colon cancer comprises the step of training a classification algorithm on a training set of cases and controls, and applying it to measured RNA abundance.
 15. The method of claims 1 to 14, wherein the classification method is a random forest method, a support vector machine (SVM), or a K-nearest neighbor method (K-NN), such as a 3-nearest neighbor method (3-NN).
 16. The method of claims 1 to 15, wherein the RNA is mRNA, cDNA, micro RNA, small nuclear RNA, unspliced RNA, or its fragments.
 17. Use of a method of claims 1 to 16 for detection of colorectal cancer in a human subject based on RNA from a blood sample.
 18. A microarray, comprising a solid support and a set of oligonucleotide probes, the set containing from 8 to about 3,000 probes, and including at least 8 probes selected from table 1 or
 8. 19. Use of a microarray for detection of colorectal cancer in a human subject based on RNA from a blood sample, comprising measuring the abundance of at least 8 RNAs listed in table 1, wherein the microarray comprises at least 1 probe for measuring the abundance of each of at least 8 RNAs.
 20. A kit for the detection of colorectal cancer in a human subject based on RNA obtained from a blood sample, comprising means for measuring the abundance of at least 8 RNAs that are chosen from the RNAs listed in table 1, preferably comprising means for exclusively measuring the abundance of RNAs that are chosen from table
 1. 21. Use of a kit of claim 20 for the detection of colorectal cancer in a human subject based on RNA from a blood sample, comprising means for measuring the abundance of at least 8 RNAs that are chosen from the RNAs listed in table 1, comprising measuring the abundance of at least 8 RNAs in a blood sample from a human subject, wherein the at least 8 RNAs are chosen from the RNAs listed in table 1, and concluding based on the measured abundance whether the subject has colorectal cancer.
 22. A method for preparing an RNA expression profile that is indicative of the presence or absence of colorectal cancer in a subject, comprising: isolating RNA from a blood sample obtained from the subject, and determining the abundance of from 8 to about 3000 RNAs, including at least 8 RNAs selected from table
 1. 